research-article

Forecasting big time series: old and new

Authors:

Christos Faloutsos,

Tim Januschowski, and

Yuyang WangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 11, Issue 12

Pages 2102 - 2105

https://doi.org/10.14778/3229863.3229878

Published: 01 August 2018 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, call centers, 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 data sources, posing unprecedented challenges to traditional time series forecasting methods. As such, 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, we also present technical details underlying these powerful tools.

References

[1]

J.-H. Böse, V. Flunkert, J. Gasthaus, T. Januschowski, D. Lange, D. Salinas, S. Schelter, M. Seeger, and Y. Wang. Probabilistic demand forecasting at scale. PVLDB, 10(12):1694--1705, 2017.

Digital Library

[2]

G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.

[3]

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

[4]

M. R. de Araujo, P. M. P. Ribeiro, and C. Faloutsos. Tensorcast: Forecasting with context using coupled tensors. In Data Mining (ICDM), 2017 IEEE International Conference on, pages 71--80. IEEE, 2017.

[5]

J. Durbin and S. J. Koopman. Time series analysis by state space methods, volume 38. OUP Oxford, 2012.

[6]

V. Flunkert, D. Salinas, and J. Gasthaus. Deepar: Probabilistic forecasting with autoregressive recurrent networks. arXiv preprint arXiv:1704.04110, 2017.

[7]

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

Digital Library

[8]

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

[9]

T. Hill, L. Marquez, M. O'Connor, and W. Remus. Artificial neural network models for forecasting and decision making. International journal of forecasting, 10(1):5--15, 1994.

[10]

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

Digital Library

[11]

R. Hyndman, A. B. Koehler, J. K. Ord, and R. D. Snyder. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media, 2008.

[12]

R. J. Hyndman and G. Athanasopoulos. Forecasting: Principles and practice. otexts; 2014. www. otexts. org/fpp., 987507109, 2017.

[13]

T. Januschowski, D. Arpin, D. Salinas, V. Flunkert, J. Gasthaus, L. Stella, and P. 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.

[14]

J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. ACM SIGKDD, 2009.

Digital Library

[15]

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

Digital Library

[16]

L. Li, B. A. Prakash, and C. Faloutsos. Parsimonious linear fingerprinting for time series. PVLDB, 3(1--2):385--396, 2010.

Digital Library

[17]

Y. Li, R. Yu, C. Shahabi, and Y. Liu. Graph convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017.

[18]

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.

[19]

L. Ma, D. Van Aken, A. Hefny, G. Mezerhane, A. Pavlo, and G. J. Gordon. Query-based workload forecasting for self-driving database management systems. In SIGMOD, pages 631--645. ACM, 2018.

Digital Library

[20]

Y. Matsubara, Y. Sakurai, and C. Faloutsos. The web as a jungle: Non-linear dynamical systems for co-evolving online activities. In WWW, pages 721--731. ACM, 2015.

Digital Library

[21]

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

Digital Library

[22]

Y. Matsubara, Y. Sakurai, C. Faloutsos, T. Iwata, and M. Yoshikawa. Fast mining and forecasting of complex time-stamped events. In KDD, pages 271--279. ACM, 2012.

Digital Library

[23]

Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: model and implications. In KDD, pages 6--14. ACM, 2012.

Digital Library

[24]

Y. Matsubara, Y. Sakurai, W. G. van Panhuis, and C. Faloutsos. Funnel: automatic mining of spatially coevolving epidemics. In KDD, pages 105--114. ACM, 2014.

Digital Library

[25]

S. Mukherjee, D. Shankar, A. Ghosh, N. Tathawadekar, P. Kompalli, S. Sarawagi, and K. Chaudhury. Armdn: Associative and recurrent mixture density networks for eretail demand forecasting. arXiv preprint arXiv:1803.03800, 2018.

[26]

S. Papadimitriou, A. Brockwell, and C. Faloutsos. Adaptive, hands-off stream mining. In VLDB, pages 560--571. Morgan Kaufmann, 2003.

Digital Library

[27]

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

Digital Library

[28]

D. C. Park, M. El-Sharkawi, R. Marks, L. Atlas, and M. Damborg. Electric load forecasting using an artificial neural network. IEEE transactions on Power Systems, 6(2):442--449, 1991.

[29]

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

[30]

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

[31]

M. Seeger, S. Rangapuram, Y. Wang, D. Salinas, J. Gasthaus, T. Januschowski, and V. Flunkert. Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale. arXiv preprint arXiv:1709.07638, 2017.

[32]

M. W. Seeger, D. Salinas, and V. Flunkert. Bayesian intermittent demand forecasting for large inventories. In NIPS, pages 4646--4654, 2016.

Digital Library

[33]

K. Takeuchi, H. Kashima, and N. Ueda. Autoregressive tensor factorization for spatio-temporal predictions. In ICDM, pages 1105--1110. IEEE, 2017.

[34]

Y. Tao, C. Faloutsos, D. Papadias, and B. Liu. Prediction and indexing of moving objects with unknown motion patterns. In SIGMOD Conference, pages 611--622. ACM, 2004.

Digital Library

[35]

D. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang. Automatic database management system tuning through large-scale machine learning. In Proceedings of the 2017 ACM International Conference on Management of Data, pages 1009--1024. ACM, 2017.

Digital Library

[36]

A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu. Wavenet: A generative model for raw audio. In SSW, page 125, 2016.

[37]

R. Wen, K. Torkkola, and B. Narayanaswamy. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053, 2017.

[38]

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. In NIPS, pages 802--810, 2015.

Digital Library

[39]

B. Yi, N. Sidiropoulos, T. Johnson, H. 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

[40]

H.-F. Yu, N. Rao, and I. S. Dhillon. Temporal regularized matrix factorization for high-dimensional time series prediction. In NIPS, pages 847--855, 2016.

Digital Library

[41]

R. Yu, S. Zheng, A. Anandkumar, and Y. Yue. Long-term forecasting using tensor-train rnns. arXiv preprint arXiv:1711.00073, 2017.

[42]

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

[43]

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

Digital Library

Cited By

Zhong SSong SZhuo WLi GLiu YChan S(2024)A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisProceedings of the VLDB Endowment10.14778/3654621.365463717:7(1723-1736)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654637
Cheng YChen PGuo CZhao KWen QYang BJensen C(2023)Weakly Guided Adaptation for Robust Time Series ForecastingProceedings of the VLDB Endowment10.14778/3636218.363623117:4(766-779)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636231
Khelifati AKhayati MDignös ADifallah DCudré-Mauroux P(2023)TSM-Bench: Benchmarking Time Series Database Systems for Monitoring ApplicationsProceedings of the VLDB Endowment10.14778/3611479.361153216:11(3363-3376)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611532
Show More Cited By

Recommendations

Time series forecasting by a seasonal support vector regression model

The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time series forecasting has not been widely investigated. This ...
Read More
Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting

Seasonal autoregressive integrated moving average (SARIMA) models form one of the most popular and widely used seasonal time series models over the past three decades. However, in several researches it has been argued that they have two basic ...
Read More
A big data framework for stock price forecasting using fuzzy time series

Stock price forecasting is the most difficult field owing to irregularities. Therefore, the stock price forecasting and recommendation is an extremely challenging task. In this paper, a big data framework for stock price forecasting using fuzzy time ...
Read More

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 11, Issue 12

August 2018

426 pages

ISSN:2150-8097

Editors:
Sihem Amer-Yahia
University of Grenoble Alpes, CNRS
,
Jian Pei
Simon Fraser University

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 August 2018

Published in PVLDB Volume 11, Issue 12

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
1,313
Total Downloads

Downloads (Last 12 months)146
Downloads (Last 6 weeks)17

Other Metrics

View Author Metrics

Citations

Cited By

Zhong SSong SZhuo WLi GLiu YChan S(2024)A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisProceedings of the VLDB Endowment10.14778/3654621.365463717:7(1723-1736)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654637
Cheng YChen PGuo CZhao KWen QYang BJensen C(2023)Weakly Guided Adaptation for Robust Time Series ForecastingProceedings of the VLDB Endowment10.14778/3636218.363623117:4(766-779)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636231
Khelifati AKhayati MDignös ADifallah DCudré-Mauroux P(2023)TSM-Bench: Benchmarking Time Series Database Systems for Monitoring ApplicationsProceedings of the VLDB Endowment10.14778/3611479.361153216:11(3363-3376)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611532
Illahi GVaishnav AKämäräinen TSiekkinen MDi Francesco MZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)Learning to Predict Head Pose in Remotely-Rendered Virtual RealityProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3590972(27-38)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3587819.3590972
Benidis KRangapuram SFlunkert VWang YMaddix DTurkmen CGasthaus JBohlke-Schneider MSalinas DStella LAubet FCallot LJanuschowski T(2022)Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyACM Computing Surveys10.1145/353338255:6(1-36)Online publication date: 19-May-2022
https://dl.acm.org/doi/10.1145/3533382
Fan CZhu JElahi HYang LLi B(2021)A Hybridly Optimized LSTM-Based Data Flow Prediction Model for Dependable Online TicketingWireless Communications & Mobile Computing10.1155/2021/99516072021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9951607
Berns FStrueber JBeecks C(2021)Local Gaussian Process Model Inference Classification for Time Series DataProceedings of the 33rd International Conference on Scientific and Statistical Database Management10.1145/3468791.3468839(209-213)Online publication date: 6-Jul-2021
https://dl.acm.org/doi/10.1145/3468791.3468839
Dong HQin SXu YQiao BZhou SYang XLuo CZhao PLin QZhang HAbuduweili ARamanujan SSubramanian KZhou ARajmohan SZhang DMoscibroda TSpinellis DGousios GChechik MDi Penta M(2021)Effective low capacity status prediction for cloud systemsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473917(1236-1241)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473917
Bohlke-Schneider MKapoor SJanuschowski T(2020)Resilient Neural Forecasting SystemsProceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning10.1145/3399579.3399869(1-5)Online publication date: 14-Jun-2020
https://dl.acm.org/doi/10.1145/3399579.3399869
Faloutsos CFlunkert VGasthaus JJanuschowski TWang Y(2020)Forecasting Big Time Series: Theory and PracticeCompanion Proceedings of the Web Conference 202010.1145/3366424.3383118(320-321)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3383118
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents