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Mining and Forecasting of Big Time-series Data

Yasushi Sakurai, Yasuko Matsubara, Christos Faloutsos
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
of time-series mining and tensor analysis.  ...  Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, ARL, or other  ... 
doi:10.1145/2723372.2731081 dblp:conf/sigmod/SakuraiMF15 fatcat:dsmv2sqs35bm5ifeqr4qnuz6ty

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018 Energies  
In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns.  ...  To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting.  ...  Figure 1 . 1 Model: incremental progressive data mining and forecasting using energy time series.  ... 
doi:10.3390/en11020452 fatcat:m3zhtmxgajcoxkyopartzpcglq

Big data and time series: A literature review paper
'Big data' i vremenske serije - literaturni pregled

Ajla Kirlić, Muhedin Hadžić
2017 Univerzitetska misao - casopis za nauku kulturu i umjetnost Novi Pazar  
This paper aims to go through the literature relating to large data, time series and various methods of analyzing large data using data mining.  ...  The best way to deal with large data is to use a large data analysis, which includes a time series methodology.  ...  In last few decades, communities of researchers are well-known with topic of time series, also there are some new reveals where and how time series can be used: • If we combine big data and time series  ... 
doi:10.5937/univmis1716139k fatcat:5cydi7d5rjguxim2rtykyhli6m

Forecasting big time series

Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang
2018 Proceedings of the VLDB Endowment  
This shift can be attributed to the availability of large, rich, and diverse time series data sources, posing unprecedented challenges to traditional time series forecasting methods.  ...  Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future  ...  ., (a) Mining and Forecasting of Big Time Series Data, by Yasuhi Sakurai, Yasuko Matsubara, and Christos Faloutsos, SIGMOD 2015, WWW 2016, KDD 2017, (b) Indexing and Mining Streams, by Christos Faloutsos  ... 
doi:10.14778/3229863.3229878 fatcat:dd3pfj3tgfckxcvb23622o5rnq

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

Jorge Ángel González Ordiano, Andreas Bartschat, Nicole Ludwig, Eric Braun, Simon Waczowicz, Nicolas Renkamp, Nico Peter, Clemens Düpmeier, Ralf Mikut, Veit Hagenmeyer
2018 Journal of Big Data  
Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented.  ...  The present article describes a concept for the creation and application of energy forecasting models in a distributed environment.  ...  Acknowledgments The authors acknowledge the support given by the "Deutsche Forschungsgemeinschaft" and by the Open Access Publishing Fund of the Karlsruhe Institute of Technology.  ... 
doi:10.1186/s40537-018-0119-6 fatcat:wofo5vx6lbcn7j3j3jht7wn7fy

Data Mining Methods and Models for Social and Economic Processes Forecasting

2018 Mechanism of an economic regulation  
This article discusses the role of Data Mining in social and economic processes, as well as the potential of using Big Data in a business environment.  ...  ARIMA models cover a sufficiently wide range of time series, and small modifications of these models allow seasoning time series to be more accurately described.  ...  cluster analysis; forecasting of time series with automatic choice of forecasting and taking into account seasonal change of indicators; association rules search algorithm.  ... 
doi:10.21272/mer.2018.80.03 fatcat:epbez4jiovbkjdpbfmm4pf5zqa

Forecasting with Big Data: A Review

Hossein Hassani, Emmanuel Sirimal Silva
2015 Annals of Data Science  
process of obtaining meaningful forecasts from Big Data.  ...  The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks  ...  Applications of Statistical and Data Mining Techniques for Big Data Forecasting In this section we identify existing applications of statistical and Data Mining techniques for forecasting with Big Data  ... 
doi:10.1007/s40745-015-0029-9 fatcat:olpzvj4ctrhypdtaymmggyt6ye

Data Science and Big Data in Energy Forecasting

Francisco Martínez-Álvarez, Alicia Troncoso, José Riquelme
2018 Energies  
This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI's Energies journal.  ...  Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.  ...  Without their support the efficient handling of all received manuscripts (article average processing time was 46.1 days), it would not have been possible to publish this special issue.  ... 
doi:10.3390/en11113224 fatcat:4fg6wspr2zgyndep73o4cyxyjq

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Kaiyan Lin, Zhao Kaifa
2022 Computational Intelligence and Neuroscience  
Using data mining technology, time series data analysis methods combined with artificial intelligence analysis, the development trend of regional industries is obtained, and then the development trend  ...  and future trend of the industrial economy in a timely and effective manner.  ...  Acknowledgments is work was supported by the Shengda Trade Economics and Management College of Zhengzhou.  ... 
doi:10.1155/2022/7400797 pmid:35898787 pmcid:PMC9313906 fatcat:o7poyrbsbzgrpibw427guhbyga

Mining Big Data: Future Forecast of Weather

Apurva Sehgal, Kanika Khurana, Cherry Singh, Ankur Kr Aggarwal
2018 Zenodo  
a rancher the best time to sow the seeds for germination, it also helps to select the relevant data which has to be send to the airplane and also helps to determine that at what time the airplane has  ...  An assortment of information mining instruments and strategies are accessible in the business, however with a restricted use in the meteorological business.  ...  These are of two kinds: time series method and associative method. Time series method: Time series methodtakes the past patterns and with the help of these patterns the future patterns are predicted.  ... 
doi:10.5281/zenodo.1745433 fatcat:wsmdld4xgvgh5n2z7biyj6azq4

A Study on Various Methods for Mining Energy Consumption pattern from Smart Home Big Data

Jemeema Maria John, Hima Anns Roy
2018 IOP Conference Series: Materials Science and Engineering  
Data mining techniques can be used to get relevant information from the smart home big data. Data mining helps in easily predicting the consumption patterns of various equipments.  ...  It helps in analyzing the behaviour of inhabitants and that of the equipments. Smart meters in homes collect large volume of data each day.  ...  Data mining helps in extracting the energy patterns from smart home big data. It also helps in forecasting the consumption patterns of energy usage.  ... 
doi:10.1088/1757-899x/396/1/012023 fatcat:qjv6b3sqkbajjoab7bshukpsre

Scalable Forecasting Techniques Applied to Big Electricity Time Series [chapter]

Antonio Galicia, José F. Torres, Francisco Martínez-Álvarez, Alicia Troncoso
2017 Lecture Notes in Computer Science  
This paper presents different scalable methods to predict time series of very long length such as time series with a high sampling frequency.  ...  Then, representative forecasting methods of different nature have been chosen such as models based on trees, two ensembles techniques (gradient-boosted trees and random forests), and a linear regression  ...  The authors would like to thank the Spanish Ministry of Economy and Competitiveness and Junta de Andalucía for the support under projects TIN2014-55894-C2-R and P12-TIC-1728, respectively.  ... 
doi:10.1007/978-3-319-59147-6_15 fatcat:yyj73atzkfdljpe5kxahjpnksq

A Comparative Study of Statistical Analysis on Big Mart using Data Mining Techniques

2020 International Journal of Advanced Trends in Computer Science and Engineering  
to use historical transaction data to forecast sales.  ...  First we conduct Exploratory Data Analysis to understand the nature of thAfter this, several traditional and novel data mining techniques have been applied on this data set, namely, linear regression,  ...  Second we found out that the data is sparse, and has no real trend. Hence, for time series forecasting in this particular data set, we can use both XGBoost and ARIMA.  ... 
doi:10.30534/ijatcse/2020/253952020 fatcat:wr26prczhrhq7khbnehr3gwxmq

Establishment of grey-neural network forecasting model of coal and gas outburst

Yang Sheng-qiang, Sun Yan, Chen Zu-yun, Yu Bao-hai, Xu Quan
2009 Procedia Earth and Planetary Science  
Meanwhile, we take coal and gas outburst instances of Yunnan Enhong coal mine as forecasting samples and compare the forecasting result from these samples with that from the conventional method, indicating  ...  that this model can meet the forecasting requirements of coal and gas outburst.  ...  Acknowledgements Project supported by the Natural Science Hall of Yunnan Province (No 2005IT02) and by the Natural Science Key-Foundation (No 50834005).  ... 
doi:10.1016/j.proeps.2009.09.025 fatcat:u4eaxhlp4ndmhlrjxxo4ygkod4

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Mahya Seyedan, Fereshteh Mafakheri
2020 Journal of Big Data  
We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines  ...  In doing so, there is a growing attention to analysis of consumption behavior and preferences using forecasts obtained from customer data and transaction records in order to manage products supply chains  ...  Acknowledgements The authors are very much thankful to anonymous reviewers whose comments and suggestion were very helpful in improving the quality of the manuscript.  ... 
doi:10.1186/s40537-020-00329-2 fatcat:62km562vjzaafowywx6q3vysnq
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