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time series</title> <meta name="description" content="Search results for: multivariate time series"> <meta name="keywords" content="multivariate time series"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="multivariate time series"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 20292</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: multivariate time series</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20292</span> Classification of Generative Adversarial Network Generated Multivariate Time Series Data Featuring Transformer-Based Deep Learning Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thrivikraman%20Aswathi">Thrivikraman Aswathi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Advaith"> S. Advaith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a> </p> <a href="https://publications.waset.org/abstracts/157460/classification-of-generative-adversarial-network-generated-multivariate-time-series-data-featuring-transformer-based-deep-learning-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20291</span> Multi-scale Spatial and Unified Temporal Feature-fusion Network for Multivariate Time Series Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Yang">Hang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyang%20Lei"> Tianyang Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20system" title=" industrial system"> industrial system</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/193205/multi-scale-spatial-and-unified-temporal-feature-fusion-network-for-multivariate-time-series-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193205.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20290</span> A Posteriori Trading-Inspired Model-Free Time Series Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Plessen%20Mogens%20Graf">Plessen Mogens Graf </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20segmentation" title="time series segmentation">time series segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=model-free" title=" model-free"> model-free</a>, <a href="https://publications.waset.org/abstracts/search?q=trading-inspired" title=" trading-inspired"> trading-inspired</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20data" title=" multivariate data"> multivariate data</a> </p> <a href="https://publications.waset.org/abstracts/118916/a-posteriori-trading-inspired-model-free-time-series-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20289</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20288</span> Small Target Recognition Based on Trajectory Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Alkentar">Saad Alkentar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulkareem%20Assalem"> Abdulkareem Assalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20targets" title="small targets">small targets</a>, <a href="https://publications.waset.org/abstracts/search?q=drones" title=" drones"> drones</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20information" title=" trajectory information"> trajectory information</a>, <a href="https://publications.waset.org/abstracts/search?q=TBD" title=" TBD"> TBD</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a> </p> <a href="https://publications.waset.org/abstracts/184545/small-target-recognition-based-on-trajectory-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20287</span> An Approach for Pattern Recognition and Prediction of Information Diffusion Model on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amartya%20Hatua">Amartya Hatua</a>, <a href="https://publications.waset.org/abstracts/search?q=Trung%20Nguyen"> Trung Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Sung"> Andrew Sung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=DTW" title=" DTW"> DTW</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20diffusion" title=" information diffusion"> information diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=RNN" title=" RNN"> RNN</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20clustering" title=" time series clustering"> time series clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/80797/an-approach-for-pattern-recognition-and-prediction-of-information-diffusion-model-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80797.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20286</span> Multivariate Genome-Wide Association Studies for Identifying Additional Loci for Myopia </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiao%20Fan">Qiao Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaobo%20Guo"> Xiaobo Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Junxian%20Zhu"> Junxian Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohu%20Ding"> Xiaohu Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Yu%20Cheng"> Ching-Yu Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Tien-Yin%20Wong"> Tien-Yin Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingguang%20He"> Mingguang He</a>, <a href="https://publications.waset.org/abstracts/search?q=Heping%20Zhang"> Heping Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xueqin%20Wang"> Xueqin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A systematic, simultaneous analysis of multiple phenotypes in genome-wide association studies (GWASs) draws a great attention to integrate the signals from single phenotypes with increased power. However, lacking an interpretable and efficient multivariate GWAS analysis impede the application of such approach. In this study, we propose to decompose the multivariate model into a series of simple univariate models. This transformation illuminates what exactly the individual trait contributes to the significant signals from the multivariate analyses. By employing our approach in the analysis of three myopia-related endophenotypes from the Singapore Malay Eye Study (SIMES), we identify novel candidate loci which were successfully validated in an independent Guangzhou Twin Eye Study (GTES). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GWAS%20multivariate" title="GWAS multivariate">GWAS multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20traits" title=" multiple traits"> multiple traits</a>, <a href="https://publications.waset.org/abstracts/search?q=myopia" title=" myopia"> myopia</a>, <a href="https://publications.waset.org/abstracts/search?q=association" title=" association"> association</a> </p> <a href="https://publications.waset.org/abstracts/79085/multivariate-genome-wide-association-studies-for-identifying-additional-loci-for-myopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79085.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">224</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20285</span> On the Impact of Oil Price Fluctuations on Stock Markets: A Multivariate Long-Memory GARCH Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manel%20Youssef">Manel Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Belkacem"> Lotfi Belkacem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oil%20prices" title="oil prices">oil prices</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20indices%20returns" title=" stock indices returns"> stock indices returns</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20volatility" title=" oil volatility"> oil volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=contagion" title=" contagion"> contagion</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC-multivariate%20%28FI%29%20GARCH" title=" DCC-multivariate (FI) GARCH"> DCC-multivariate (FI) GARCH</a> </p> <a href="https://publications.waset.org/abstracts/20756/on-the-impact-of-oil-price-fluctuations-on-stock-markets-a-multivariate-long-memory-garch-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20756.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20284</span> Distributed Perceptually Important Point Identification for Time Series Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tak-Chung%20Fu">Tak-Chung Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying-Kit%20Hung"> Ying-Kit Hung</a>, <a href="https://publications.waset.org/abstracts/search?q=Fu-Lai%20Chung"> Fu-Lai Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20computing" title="distributed computing">distributed computing</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20analysis" title=" performance analysis"> performance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Perceptually%20Important%20Point%20identification" title=" Perceptually Important Point identification"> Perceptually Important Point identification</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20data%20mining" title=" time series data mining"> time series data mining</a> </p> <a href="https://publications.waset.org/abstracts/84358/distributed-perceptually-important-point-identification-for-time-series-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84358.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20283</span> Quantum Statistical Machine Learning and Quantum Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Alzeley">Omar Alzeley</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Utev"> Sergey Utev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20techniques" title=" simulation techniques"> simulation techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20probability" title=" quantum probability"> quantum probability</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20product" title=" tensor product"> tensor product</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/52720/quantum-statistical-machine-learning-and-quantum-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">469</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20282</span> Time Series Forecasting (TSF) Using Various Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jimeng%20Shi">Jimeng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahek%20Jain"> Mahek Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Giri%20Narasimhan"> Giri Narasimhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality%20prediction" title="air quality prediction">air quality prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20algorithms" title=" deep learning algorithms"> deep learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=look-back%20window" title=" look-back window"> look-back window</a> </p> <a href="https://publications.waset.org/abstracts/146879/time-series-forecasting-tsf-using-various-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20281</span> R Software for Parameter Estimation of Spatio-Temporal Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Budi%20Nurani%20Ruchjana">Budi Nurani Ruchjana</a>, <a href="https://publications.waset.org/abstracts/search?q=Atje%20Setiawan%20Abdullah"> Atje Setiawan Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Gede%20Nyoman%20Mindra%20Jaya"> I. Gede Nyoman Mindra Jaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Eddy%20Hermawan"> Eddy Hermawan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GSTAR%20Model" title="GSTAR Model">GSTAR Model</a>, <a href="https://publications.waset.org/abstracts/search?q=MAPE" title=" MAPE"> MAPE</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS%20method" title=" OLS method"> OLS method</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20production" title=" oil production"> oil production</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20software" title=" R software"> R software</a> </p> <a href="https://publications.waset.org/abstracts/62320/r-software-for-parameter-estimation-of-spatio-temporal-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20280</span> Chern-Simons Equation in Financial Theory and Time-Series Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ognjen%20Vukovic">Ognjen Vukovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chern-Simons equation represents the cornerstone of quantum physics. The question that is often asked is if the aforementioned equation can be successfully applied to the interaction in international financial markets. By analysing the time series in financial theory, it is proved that Chern-Simons equation can be successfully applied to financial time-series. The aforementioned statement is based on one important premise and that is that the financial time series follow the fractional Brownian motion. All variants of Chern-Simons equation and theory are applied and analysed. Financial theory time series movement is, firstly, topologically analysed. The main idea is that exchange rate represents two-dimensional projections of three-dimensional Brownian motion movement. Main principles of knot theory and topology are applied to financial time series and setting is created so the Chern-Simons equation can be applied. As Chern-Simons equation is based on small particles, it is multiplied by the magnifying factor to mimic the real world movement. Afterwards, the following equation is optimised using Solver. The equation is applied to n financial time series in order to see if it can capture the interaction between financial time series and consequently explain it. The aforementioned equation represents a novel approach to financial time series analysis and hopefully it will direct further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brownian%20motion" title="Brownian motion">Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=Chern-Simons%20theory" title=" Chern-Simons theory"> Chern-Simons theory</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20time%20series" title=" financial time series"> financial time series</a>, <a href="https://publications.waset.org/abstracts/search?q=econophysics" title=" econophysics"> econophysics</a> </p> <a href="https://publications.waset.org/abstracts/30127/chern-simons-equation-in-financial-theory-and-time-series-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">473</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20279</span> Investigation on Performance of Change Point Algorithm in Time Series Dynamical Regimes and Effect of Data Characteristics </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi">Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi"> Mohammad Javad Mollakazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reﬂected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuation%20in%20statistical%20characteristics" title=" fluctuation in statistical characteristics"> fluctuation in statistical characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20learning" title=" optimal learning"> optimal learning</a>, <a href="https://publications.waset.org/abstracts/search?q=change-point%20algorithm" title=" change-point algorithm"> change-point algorithm</a> </p> <a href="https://publications.waset.org/abstracts/18167/investigation-on-performance-of-change-point-algorithm-in-time-series-dynamical-regimes-and-effect-of-data-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18167.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20278</span> Nonstationarity Modeling of Economic and Financial Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Slim">C. Slim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional techniques for analyzing time series are based on the notion of stationarity of phenomena under study, but in reality most economic and financial series do not verify this hypothesis, which implies the implementation of specific tools for the detection of such behavior. In this paper, we study nonstationary non-seasonal time series tests in a non-exhaustive manner. We formalize the problem of nonstationary processes with numerical simulations and take stock of their statistical characteristics. The theoretical aspects of some of the most common unit root tests will be discussed. We detail the specification of the tests, showing the advantages and disadvantages of each. The empirical study focuses on the application of these tests to the exchange rate (USD/TND) and the Consumer Price Index (CPI) in Tunisia, in order to compare the Power of these tests with the characteristics of the series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stationarity" title="stationarity">stationarity</a>, <a href="https://publications.waset.org/abstracts/search?q=unit%20root%20tests" title=" unit root tests"> unit root tests</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20time%20series" title=" economic time series"> economic time series</a>, <a href="https://publications.waset.org/abstracts/search?q=ADF%20tests" title=" ADF tests"> ADF tests</a> </p> <a href="https://publications.waset.org/abstracts/77063/nonstationarity-modeling-of-economic-and-financial-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20277</span> Hierarchical Piecewise Linear Representation of Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vineetha%20Bettaiah">Vineetha Bettaiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Heggere%20S.%20Ranganath"> Heggere S. Ranganath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20linear%20representation" title=" piecewise linear representation"> piecewise linear representation</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20representation" title=" time series representation"> time series representation</a> </p> <a href="https://publications.waset.org/abstracts/2680/hierarchical-piecewise-linear-representation-of-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20276</span> pscmsForecasting: A Python Web Service for Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Andrianakis">Ioannis Andrianakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasileios%20Gkatas"> Vasileios Gkatas</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikos%20Eleftheriadis"> Nikos Eleftheriadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexios%20Ellinidis"> Alexios Ellinidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ermioni%20Avramidou"> Ermioni Avramidou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source" title=" open source"> open source</a> </p> <a href="https://publications.waset.org/abstracts/170621/pscmsforecasting-a-python-web-service-for-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20275</span> The Use of Boosted Multivariate Trees in Medical Decision-Making for Repeated Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebru%20Turgal">Ebru Turgal</a>, <a href="https://publications.waset.org/abstracts/search?q=Beyza%20Doganay%20Erdogan"> Beyza Doganay Erdogan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boosted%20multivariate%20trees" title="boosted multivariate trees">boosted multivariate trees</a>, <a href="https://publications.waset.org/abstracts/search?q=longitudinal%20data" title=" longitudinal data"> longitudinal data</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20regression%20tree" title=" multivariate regression tree"> multivariate regression tree</a>, <a href="https://publications.waset.org/abstracts/search?q=panel%20data" title=" panel data"> panel data</a> </p> <a href="https://publications.waset.org/abstracts/87009/the-use-of-boosted-multivariate-trees-in-medical-decision-making-for-repeated-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20274</span> Regression for Doubly Inflated Multivariate Poisson Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishapathik%20Das">Ishapathik Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumen%20Sen"> Sumen Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Rao%20Chaganty"> N. Rao Chaganty</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Sengupta"> Pooja Sengupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dependent multivariate count data occur in several research studies. These data can be modeled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells, and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present a real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copula" title="copula">copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20distributions" title=" multivariate distributions"> multivariate distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=inflated%20distributios" title=" inflated distributios"> inflated distributios</a> </p> <a href="https://publications.waset.org/abstracts/105114/regression-for-doubly-inflated-multivariate-poisson-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20273</span> Co-Integration Model for Predicting Inflation Movement in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salako%20Rotimi">Salako Rotimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Oshungade%20Stephen"> Oshungade Stephen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ojewoye%20Opeyemi"> Ojewoye Opeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic" title="economic">economic</a>, <a href="https://publications.waset.org/abstracts/search?q=inflation" title=" inflation"> inflation</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=series" title=" series"> series</a> </p> <a href="https://publications.waset.org/abstracts/109868/co-integration-model-for-predicting-inflation-movement-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109868.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20272</span> Determinants of Aggregate Electricity Consumption in Ghana: A Multivariate Time Series Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Renata%20Konadu">Renata Konadu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Ghana, electricity has become the main form of energy which all sectors of the economy rely on for their businesses. Therefore, as the economy grows, the demand and consumption of electricity also grow alongside due to the heavy dependence on it. However, since the supply of electricity has not increased to match the demand, there has been frequent power outages and load shedding affecting business performances. To solve this problem and advance policies to secure electricity in Ghana, it is imperative that those factors that cause consumption to increase be analysed by considering the three classes of consumers; residential, industrial and non-residential. The main argument, however, is that, export of electricity to other neighbouring countries should be included in the electricity consumption model and considered as one of the significant factors which can decrease or increase consumption. The author made use of multivariate time series data from 1980-2010 and econometric models such as Ordinary Least Squares (OLS) and Vector Error Correction Model. Findings show that GDP growth, urban population growth, electricity exports and industry value added to GDP were cointegrated. The results also showed that there is unidirectional causality from electricity export and GDP growth and Industry value added to GDP to electricity consumption in the long run. However, in the short run, there was found to be a directional causality among all the variables and electricity consumption. The results have useful implication for energy policy makers especially with regards to electricity consumption, demand, and supply. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20consumption" title="electricity consumption">electricity consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20policy" title=" energy policy"> energy policy</a>, <a href="https://publications.waset.org/abstracts/search?q=GDP%20growth" title=" GDP growth"> GDP growth</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20error%20correction%20model" title=" vector error correction model"> vector error correction model</a> </p> <a href="https://publications.waset.org/abstracts/56657/determinants-of-aggregate-electricity-consumption-in-ghana-a-multivariate-time-series-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56657.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20271</span> Comparison of Applicability of Time Series Forecasting Models VAR, ARCH and ARMA in Management Science: Study Based on Empirical Analysis of Time Series Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Tariq">Muhammad Tariq</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Tahir"> Hammad Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawwad%20Mahmood%20Butt"> Fawwad Mahmood Butt </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20regression" title=" auto regression"> auto regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=ARMA" title=" ARMA"> ARMA</a> </p> <a href="https://publications.waset.org/abstracts/45124/comparison-of-applicability-of-time-series-forecasting-models-var-arch-and-arma-in-management-science-study-based-on-empirical-analysis-of-time-series-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20270</span> Analysis of Dynamics Underlying the Observation Time Series by Using a Singular Spectrum Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Delage">O. Delage</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Bencherif"> H. Bencherif</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Portafaix"> T. Portafaix</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Bourdier"> A. Bourdier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of time series analysis is to learn about the dynamics behind some time ordered measurement data. Two approaches are used in the literature to get a better knowledge of the dynamics contained in observation data sequences. The first of these approaches concerns time series decomposition, which is an important analysis step allowing patterns and behaviors to be extracted as components providing insight into the mechanisms producing the time series. As in many cases, time series are short, noisy, and non-stationary. To provide components which are physically meaningful, methods such as Empirical Mode Decomposition (EMD), Empirical Wavelet Transform (EWT) or, more recently, Empirical Adaptive Wavelet Decomposition (EAWD) have been proposed. The second approach is to reconstruct the dynamics underlying the time series as a trajectory in state space by mapping a time series into a set of Rᵐ lag vectors by using the method of delays (MOD). Takens has proved that the trajectory obtained with the MOD technic is equivalent to the trajectory representing the dynamics behind the original time series. This work introduces the singular spectrum decomposition (SSD), which is a new adaptive method for decomposing non-linear and non-stationary time series in narrow-banded components. This method takes its origin from singular spectrum analysis (SSA), a nonparametric spectral estimation method used for the analysis and prediction of time series. As the first step of SSD is to constitute a trajectory matrix by embedding a one-dimensional time series into a set of lagged vectors, SSD can also be seen as a reconstruction method like MOD. We will first give a brief overview of the existing decomposition methods (EMD-EWT-EAWD). The SSD method will then be described in detail and applied to experimental time series of observations resulting from total columns of ozone measurements. The results obtained will be compared with those provided by the previously mentioned decomposition methods. We will also compare the reconstruction qualities of the observed dynamics obtained from the SSD and MOD methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title="time series analysis">time series analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20time%20series%20decomposition" title=" adaptive time series decomposition"> adaptive time series decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space%20reconstruction" title=" phase space reconstruction"> phase space reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20spectrum%20analysis" title=" singular spectrum analysis"> singular spectrum analysis</a> </p> <a href="https://publications.waset.org/abstracts/147886/analysis-of-dynamics-underlying-the-observation-time-series-by-using-a-singular-spectrum-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20269</span> Proactive Pure Handoff Model with SAW-TOPSIS Selection and Time Series Predict</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harold%20V%C3%A1squez">Harold Vásquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Cesar%20Hern%C3%A1ndez"> Cesar Hernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Ingrid%20P%C3%A1ez"> Ingrid Páez </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper approach cognitive radio technic and applied pure proactive handoff Model to decrease interference between PU and SU and comparing it with reactive handoff model. Through the study and analysis of multivariate models SAW and TOPSIS join to 3 dynamic prediction techniques AR, MA ,and ARMA. To evaluate the best model is taken four metrics: number failed handoff, number handoff, number predictions, and number interference. The result presented the advantages using this type of pure proactive models to predict changes in the PU according to the selected channel and reduce interference. The model showed better performance was TOPSIS-MA, although TOPSIS-AR had a higher predictive ability this was not reflected in the interference reduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20handoff" title=" spectrum handoff"> spectrum handoff</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20networks" title=" wireless networks"> wireless networks</a> </p> <a href="https://publications.waset.org/abstracts/33216/proactive-pure-handoff-model-with-saw-topsis-selection-and-time-series-predict" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33216.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20268</span> Applying a Noise Reduction Method to Reveal Chaos in the River Flow Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20H.%20Fattahi">Mohammad H. Fattahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20behavior" title="chaotic behavior">chaotic behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title=" river flow"> river flow</a> </p> <a href="https://publications.waset.org/abstracts/12972/applying-a-noise-reduction-method-to-reveal-chaos-in-the-river-flow-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12972.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20267</span> The Modelling of Real Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valeria%20Bondarenko">Valeria Bondarenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title="mathematical model">mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20process" title=" random process"> random process</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20process" title=" Wiener process"> Wiener process</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title=" fractional Brownian motion"> fractional Brownian motion</a> </p> <a href="https://publications.waset.org/abstracts/49210/the-modelling-of-real-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20266</span> Optimal Maintenance Policy for a Partially Observable Two-Unit System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari">Leila Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20B.%20Akram%20Khaleghei"> G. B. Akram Khaleghei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1, which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM, has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition-based%20maintenance" title="condition-based maintenance">condition-based maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control%20chart" title=" multivariate Bayesian control chart"> multivariate Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title=" partially observable system"> partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=two-unit%20system" title=" two-unit system"> two-unit system</a> </p> <a href="https://publications.waset.org/abstracts/9588/optimal-maintenance-policy-for-a-partially-observable-two-unit-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">459</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20265</span> The Modality of Multivariate Skew Normal Mixture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bader%20Alruwaili">Bader Alruwaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Ray"> Surajit Ray</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Finite mixtures are a flexible and powerful tool that can be used for univariate and multivariate distributions, and a wide range of research analysis has been conducted based on the multivariate normal mixture and multivariate of a t-mixture. Determining the number of modes is an important activity that, in turn, allows one to determine the number of homogeneous groups in a population. Our work currently being carried out relates to the study of the modality of the skew normal distribution in the univariate and multivariate cases. For the skew normal distribution, the aims are associated with studying the modality of the skew normal distribution and providing the ridgeline, the ridgeline elevation function, the $\Pi$ function, and the curvature function, and this will be conducive to an exploration of the number and location of mode when mixing the two components of skew normal distribution. The subsequent objective is to apply these results to the application of real world data sets, such as flow cytometry data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mode" title="mode">mode</a>, <a href="https://publications.waset.org/abstracts/search?q=modality" title=" modality"> modality</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20skew%20normal" title=" multivariate skew normal"> multivariate skew normal</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20mixture" title=" finite mixture"> finite mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20mode" title=" number of mode"> number of mode</a> </p> <a href="https://publications.waset.org/abstracts/68912/the-modality-of-multivariate-skew-normal-mixture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20264</span> Adaptive Neuro Fuzzy Inference System Model Based on Support Vector Regression for Stock Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Setianingrum">Anita Setianingrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Oki%20S.%20Jaya"> Oki S. Jaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuherman%20Rustam"> Zuherman Rustam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series" title=" fuzzy time series"> fuzzy time series</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20forecasting" title=" stock forecasting"> stock forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=SVR" title=" SVR"> SVR</a> </p> <a href="https://publications.waset.org/abstracts/62703/adaptive-neuro-fuzzy-inference-system-model-based-on-support-vector-regression-for-stock-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62703.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">246</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20263</span> Stock Price Prediction Using Time Series Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Sen">Sumit Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohan%20Khedekar"> Sohan Khedekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Umang%20Shinde"> Umang Shinde</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivam%20Bhargava"> Shivam Bhargava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autoregressive%20Integrated%20Moving%20Average" title="Autoregressive Integrated Moving Average">Autoregressive Integrated Moving Average</a>, <a href="https://publications.waset.org/abstracts/search?q=Deep%20Learning" title=" Deep Learning"> Deep Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Long%20Short%20Term%20Memory" title=" Long Short Term Memory"> Long Short Term Memory</a>, <a href="https://publications.waset.org/abstracts/search?q=Time-series" title=" Time-series"> Time-series</a> </p> <a href="https://publications.waset.org/abstracts/137402/stock-price-prediction-using-time-series-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137402.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series&page=4">4</a></li> <li class="page-item"><a class="page-link" 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