Foundations of Sequence-to-Sequence Modeling for Time Series

Preprint English OPEN
Kuznetsov, Vitaly; Mariet, Zelda;
(2018)
  • Subject: Statistics - Machine Learning | Computer Science - Machine Learning | Computer Science - Artificial Intelligence

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the fi... View more
  • References (43)
    43 references, page 1 of 5

    [1] Marta Banbura, Domenico Giannone, and Lucrezia Reichlin. Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1):71-92, 2010.

    [2] Sumanta Basu and George Michailidis. Regularized estimation in sparse high-dimensional time series models. Ann. Statist., 43(4):1535-1567, 08 2015.

    [3] Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, and Robert Jenssen. Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis. Springer Briefs in Computer Science. Springer, 2017.

    [4] Tim Bollerslev. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3):307 - 327, 1986.

    [5] George Edward Pelham Box and Gwilym Jenkins. Time Series Analysis, Forecasting and Control. Holden-Day, Incorporated, 1990.

    [6] Peter J Brockwell and Richard A Davis. Time Series: Theory and Methods. Springer-Verlag New York, Inc., New York, NY, USA, 1986.

    [7] P. Doukhan. Mixing: Properties and Examples. Lecture notes in statistics. Springer, 1994.

    [8] Robert Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50(4):987-1007, 1982.

    [9] Valentin Flunkert, David Salinas, and Jan Gasthaus. Deepar: Probabilistic forecasting with autoregressive recurrent networks. Arxiv:1704.04110, 2017.

    [10] Mahsa Ghafarianzadeh and Claire Monteleoni. Climate prediction via matrix completion. In Late-Breaking Developments in the Field of Artificial Intelligence, volume WS-13-17 of AAAI Workshops. AAAI, 2013.

  • Metrics
    No metrics available
Share - Bookmark