research-article

Classical and Contemporary Approaches to Big Time Series Forecasting

Authors:

Christos Faloutsos,

Tim Januschowski, and

Yuyang WangAuthors Info & Claims

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

June 2019

Pages 2042 - 2047

https://doi.org/10.1145/3299869.3314033

Published: 25 June 2019 Publication History

Abstract

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 demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses and factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series corpora and result in a set of challenges that need to be addressed such as the following. How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of "similar'' time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes? The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in three related fields: (1) classical modeling of time series, (2) scalable tensor methods, and (3) deep learning for forecasting. Further, we share lessons learned from building scalable forecasting systems. While our focus is on providing an intuitive overview of the methods and practical issues which we will illustrate via case studies, we also present some technical details underlying these powerful tools.

References

[1]

John Alberg and Zachary C Lipton. Improving factor-based quantitative investing by forecasting company fundamentals. arXiv preprint arXiv:1711.04837, 2017.

[2]

Roy M. Anderson and Robert M. May. Infectious diseases of humans: Dynamics and control. Oxford Press, 2002.

[3]

Miguel Araújo, Pedro Ribeiro, and Christos Faloutsos. Tensorcast: forecasting time-evolving networks with contextual information. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pages 5199--5203. AAAI Press, 2018.

Digital Library

[4]

Albert L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435, 2005.

[5]

Frank M. Bass. A new product growth for model consumer durables. Management Science, 15(5):215--227, 1969.

Digital Library

[6]

Konstantinos Benidis, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski. Probabilistic forecasting with spline quantile function rnns. In Artificial Intelligence and Statistics, 2019.

[7]

Filippo Maria Bianchi, Enrico Maiorino, Michael C Kampffmeyer, Antonello Rizzi, and Robert Jenssen. An overview and comparative analysis of recurrent neural networks for short term load forecasting. arXiv preprint arXiv:1705.04378, 2017.

[8]

Mikołaj Bi'nkowski, Gautier Marti, and Philippe Donnat. Autoregressive convolutional neural networks for asynchronous time series. arXiv preprint arXiv:1703.04122, 2017.

[9]

Joos-Hendrik Böse, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias Seeger, and Yuyang Wang. Probabilistic demand forecasting at scale. PVLDB, 10(12):1694--1705, 2017.

Digital Library

[10]

George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.

[11]

P. J. Brockwell and R. A. Davis. Time series: Theory and method. Springer-Verlag, 1991.

Digital Library

[12]

Deepay Chakrabarti and Christos Faloutsos. F4: Large-scale automated forecasting using fractals. CIKM 2002, November 2002.

[13]

Miguel Ramos de Araujo, Pedro Manuel Pinto Ribeiro, and Christos Faloutsos. Tensorcast: Forecasting with context using coupled tensors. In Data Mining (ICDM), 2017 IEEE International Conference on, pages 71--80. IEEE, 2017.

[14]

Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, and Yan Liu. Latent space model for road networks to predict time-varying traffic. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1525--1534. ACM, 2016.

Digital Library

[15]

Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1555--1564. ACM, 2016.

Digital Library

[16]

James Durbin and Siem Jan Koopman. Time series analysis by state space methods, volume 38. OUP Oxford, 2012.

[17]

David Easley and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, September 2010.

[18]

Christos Faloutsos, Jan Gasthaus, Tim Januschowski, and Yuyang Wang. Forecasting big time series: old and new. Proceedings of the VLDB Endowment, 11(12):2102--2105, 2018.

Digital Library

[19]

Valentin Flunkert, David Salinas, Jan Gasthaus, and Tim Januschowski. Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, arXiv:1704.04110, To appear.

[20]

Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther. A disentangled recognition and nonlinear dynamics model for unsupervised learning. In Advances in Neural Information Processing Systems, pages 3604--3613, 2017.

Digital Library

[21]

Edward Gately. Neural networks for financial forecasting. John Wiley & Sons, Inc., 1995.

Digital Library

[22]

Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery. Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2):243--268, 2007.

[23]

Tilmann Gneiting and Adrian E Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359--378, 2007.

[24]

Andrew C Harvey. Forecasting, structural time series models and the Kalman filter. Cambridge university press, 1990.

[25]

Tim Hill, Leorey Marquez, Marcus O'Connor, and William Remus. Artificial neural network models for forecasting and decision making. International journal of forecasting, 10(1):5--15, 1994.

[26]

Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735--1780, 1997.

Digital Library

[27]

Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos. Beatlex: Summarizing and forecasting time series with patterns. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 3--19. Springer, 2017.

[28]

Bryan Hooi, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos. Streamcast: Fast and online mining of power grid time sequences. In Proceedings of the 2018 SIAM International Conference on Data Mining, pages 531--539. SIAM, 2018.

[29]

Rob Hyndman, Anne B Koehler, J Keith Ord, and Ralph D Snyder. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media, 2008.

[30]

Rob J Hyndman and George Athanasopoulos. Forecasting: Principles and practice. www. otexts. org/fpp., 987507109, 2017.

[31]

Ankur Jain, Edward Y. Chang, and Yuan-Fang Wang. Adaptive stream resource management using kalman filters. In SIGMOD Conference, pages 11--22, 2004.

Digital Library

[32]

Tim Januschowski, David Arpin, David Salinas, Valentin Flunkert, Jan Gasthaus, Lorenzo Stella, and Paul Vazquez. Now available in amazon sagemaker: Deepar algorithm for more accurate time series forecasting. https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/, 2018.

[33]

Tim Januschowski, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, and Laurent Callot. Deep learning for forecasting: Current trends and challenges. Foresight: The International Journal of Applied Forecasting, (51), 2018.

[34]

Tim Januschowski, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, Laurent Callot, et al. Deep learning for forecasting. Foresight: The International Journal of Applied Forecasting, (50):35--41, 2018.

[35]

Tamara G. Kolda and Brett W. Bader. Tensor decompositions and applications. SIAM Review, 51(3):455--500, 2009.

Digital Library

[36]

Tamara G. Kolda, Brett W. Bader, and Joseph P. Kenny. Higher-order web link analysis using multilinear algebra. In ICDM 2005: Proceedings of the 5th IEEE International Conference on Data Mining, pages 242--249, November 2005.

Digital Library

[37]

Rahul G Krishnan, Uri Shalit, and David Sontag. Structured inference networks for nonlinear state space models. In AAAI, pages 2101--2109, 2017.

Digital Library

[38]

Vitaly Kuznetsov and Zelda Mariet. Foundations of sequence-to-sequence modeling for time series. 2019.

[39]

Jure Leskovec, Lars Backstrom, and Jon Kleinberg. Meme-tracking and the dynamics of the news cycle. ACM SIGKDD, 2009.

Digital Library

[40]

L. Li, J. McCann, N.S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In ACM SIGKDD, pages 507--516, 2009.

Digital Library

[41]

Lei (Carnegie Mellon University) Li, B. Aditya (Carnegie Mellon University) Prakash, and Christos (Carnegie Mellon University) Faloutsos. Parsimonious Linear Fingerprinting for Time Series. In VLDB, Singapore, 2010.

[42]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Graph convolutional recurrent neural network: Data-driven traffic forecasting. ICLR, 2018.

[43]

C-N Lu, H-T Wu, and S Vemuri. Neural network based short term load forecasting. IEEE Transactions on Power Systems, 8(1):336--342, 1993.

[44]

Danielle C. Maddix, Yuyang Wang, and Alex Smola. Deep factors with gaussian processes for forecasting. In Workshop on Bayesian Deep Learning, NIPS, 2018.

[45]

Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3):1--26, 03 2018.

[46]

Yasuko Matsubara, Yasushi Sakurai, and Christos Faloutsos. Non-linear mining of competing local activities. In WWW, pages 737--747. ACM, 2016.

Digital Library

[47]

Yasuko Matsubara, Yasushi Sakurai, and Christos Faloutsos. Ecosystem on the web: non-linear mining and forecasting of co-evolving online activities. World Wide Web, 20(3):439--465, 2017.

Digital Library

[48]

Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos, Tomoharu Iwata, and Masatoshi Yoshikawa. Fast mining and forecasting of complex time-stamped events. In KDD, pages 271--279. ACM, 2012.

Digital Library

[49]

Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, and Christos Faloutsos. Rise and fall patterns of information diffusion: model and implications. In KDD, pages 6--14. ACM, 2012.

Digital Library

[50]

Yasuko Matsubara, Yasushi Sakurai, Willem G van Panhuis, and Christos Faloutsos. Funnel: automatic mining of spatially coevolving epidemics. In KDD, pages 105--114. ACM, 2014.

Digital Library

[51]

Robert May and Angela McLean. Theoretical Ecology: Principles and Applications. Oxford University Press, 3rd edition, 2007.

Digital Library

[52]

M. McGlohon, J. Leskovec, C. Faloutsos, N. Glance, and M. Hurst. Finding patterns in blog shapes and blog evolution. In International Conference on Weblogs and Social Media., Boulder, Colo., Helen Martin 2007.

[53]

Srayanta Mukherjee, Devashish Shankar, Atin Ghosh, Nilam Tathawadekar, Pramod Kompalli, Sunita Sarawagi, and Krishnendu Chaudhury. Armdn: Associative and recurrent mixture density networks for eretail demand forecasting. arXiv preprint arXiv:1803.03800, 2018.

[54]

Martin A Nowak. Evolutionary dynamics. Harvard University Press, 2006.

[55]

Spiros Papadimitriou, Anthony Brockwell, and Christos Faloutsos. Adaptive, hands-off stream mining. In VLDB, pages 560--571. Morgan Kaufmann, 2003.

Digital Library

[56]

Spiros Papadimitriou and Philip S. Yu. Optimal multi-scale patterns in time series streams. In SIGMOD Conference, pages 647--658, 2006.

Digital Library

[57]

Dong C Park, MA El-Sharkawi, RJ Marks, LE Atlas, and MJ Damborg. Electric load forecasting using an artificial neural network. IEEE transactions on Power Systems, 6(2):442--449, 1991.

[58]

Danilo Poccia. Amazon forecast -- time series forecasting made easy. https://aws.amazon.com/blogs/aws/amazon-forecast-time-series-forecasting-made-easy/, 2018.

[59]

Syama Sundar Rangapuram, Matthias Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. Deep state space models for time series forecasting. In Advances in Neural Information Processing Systems, 2018.

Digital Library

[60]

You Ren, Emily B Fox, and Andrew Bruce. Achieving a hyperlocal housing price index: Overcoming data sparsity by bayesian dynamical modeling of multiple data streams. arXiv preprint arXiv:1505.01164, 2015.

[61]

Lucas Roberts, Leo Razoumov, Lin Su, and Yuyang Wang. Gini regularized optimal transport with an application to spatio-temporal forecasting. NIPS Workshop on Optimal Transport, 2017.

[62]

Steven L Scott and Hal R Varian. Predicting the present with bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1--2):4--23, 2014.

[63]

Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, and Valentin Flunkert. Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale. arXiv preprint arXiv:1709.07638, 2017.

[64]

Matthias W Seeger, David Salinas, and Valentin Flunkert. Bayesian intermittent demand forecasting for large inventories. In NIPS, pages 4646--4654, 2016.

Digital Library

[65]

Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, and Christos Faloutsos. Powercast: Mining and forecasting power grid sequences. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 606--621. Springer, 2017.

[66]

Jimeng Sun, Dacheng Tao, and Christos Faloutsos. Beyond streams and graphs: dynamic tensor analysis. In KDD, pages 374--383. ACM, 2006.

Digital Library

[67]

Koh Takeuchi, Hisashi Kashima, and Naonori Ueda. Autoregressive tensor factorization for spatio-temporal predictions. In ICDM, pages 1105--1110. IEEE, 2017.

[68]

Yufei Tao, Christos Faloutsos, Dimitris Papadias, and Bin Liu. Prediction and indexing of moving objects with unknown motion patterns. In SIGMOD Conference, pages 611--622. ACM, 2004.

Digital Library

[69]

A"aron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw audio. In SSW, page 125, 2016.

[70]

Ruofeng Wen, Kari Torkkola, and Balakrishnan Narayanaswamy. A multi-horizon quantile recurrent forecaster. NIPS Workshop on Time Series, arXiv:1711.11053, 2017.

[71]

Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, and Hongyuan Zha. Wasserstein learning of deep generative point process models. In NIPS, pages 3247--3257, 2017.

Digital Library

[72]

SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. In NIPS, pages 802--810, 2015.

Digital Library

[73]

B.K. Yi, N.D. Sidiropoulos, T. Johnson, HV Jagadish, C. Faloutsos, and A. Biliris. Online data mining for co-evolving time sequences. In Data Engineering, 2000. Proceedings. 16th International Conference on, pages 13--22. IEEE, 2000.

Digital Library

[74]

Hsiang-Fu Yu, Nikhil Rao, and Inderjit S Dhillon. Temporal regularized matrix factorization for high-dimensional time series prediction. In NIPS, pages 847--855, 2016.

Digital Library

[75]

Rose Yu, Stephan Zheng, Anima Anandkumar, and Yisong Yue. Long-term forecasting using tensor-train rnns. arXiv preprint arXiv:1711.00073, 2017.

[76]

Guoqiang Zhang, B Eddy Patuwo, and Michael Y Hu. Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 14(1):35--62, 1998.

[77]

Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In AAAI, pages 1655--1661, 2017.

Digital Library

[78]

Yunyue Zhu and Dennis Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. PVLDB, pages 358--369, 2002.

Digital Library

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Classical and Contemporary Approaches to Big Time Series Forecasting

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cover image ACM Conferences

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

June 2019

2106 pages

ISBN:9781450356435

DOI:10.1145/3299869

General Chairs:
Peter Boncz
CWI & Vrije Universiteit Amsterdam, The Netherlands
,
Stefan Manegold
CWI & Universiteit Leiden, The Netherlands
,
Program Chairs:
Anastasia Ailamaki
EPFL, Switzerland
,
Amol Deshpande
University of Maryland, USA
,
Tim Kraska
MIT, USA

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Published: 25 June 2019

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