Orbit: Probabilistic Forecast with Exponential Smoothing

Time series forecasting is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some of those forecasting challenges, statistical methods remain powerful while dealing with low granularity data. This paper introduces a refined Bayesian exponential smoothing model with the help of probabilistic programming languages including Stan. Our model refinements include additional global trend, transformation for multiplicative form, noise distribution and choice of priors. A benchmark study is conducted on a rich set of time-series data sets for our models along with other well-known time series models.

[1]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[2]  Rob J Hyndman,et al.  Forecasting with Exponential Smoothing: The State Space Approach , 2008 .

[3]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[4]  R. Kennedy,et al.  Defense Advanced Research Projects Agency (DARPA). Change 1 , 1996 .

[5]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[6]  Lalana Kagal,et al.  Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

[7]  Rob J Hyndman,et al.  Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .

[8]  Christoph Bergmeir,et al.  Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions , 2019, ArXiv.

[9]  Steven L. Scott,et al.  Predicting the Present with Bayesian Structural Time Series , 2013 .

[10]  K. P. Soman,et al.  Stock price prediction using LSTM, RNN and CNN-sliding window model , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[11]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[12]  Evangelos Spiliotis,et al.  The M4 Competition: Results, findings, conclusion and way forward , 2018, International Journal of Forecasting.

[13]  Noah D. Goodman,et al.  Pyro: Deep Universal Probabilistic Programming , 2018, J. Mach. Learn. Res..

[14]  John F. MacGregor,et al.  Some Recent Advances in Forecasting and Control , 1968 .

[15]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[16]  Wei Xu,et al.  Bidirectional LSTM-CRF Models for Sequence Tagging , 2015, ArXiv.

[17]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[18]  Benjamin Letham,et al.  Forecasting at Scale , 2018, PeerJ Prepr..