ACML 2020 Tutorial: Forecasting for Data Scientists

18 November 2020


Though machine learners claim for potentially decades that their methods yield great performance for time series forecasting, until recently machine learning methods were not able to outperform even simple benchmarks in forecasting competitions, and did not play a role in practical applications. This has changed in the last 3-4 years, with methods being able to win several prestigious competitions. The models are now competitive as more series, and longer series due to higher sampling rates, are typically available. In this tutorial, we will briefly recap the history of the field of forecasting and its developments parallel to machine learning, and then discuss recent developments in the field, around learning across series with global models, Machine Learning methods such as recurrent neural networks, CNNs, and other models, and how they are now able to outperform traditional methods. We further will look into the intricacies of forecast evaluation, and into more advanced topics such as hierarchical forecasting and multivariate forecasting.

Intended audience

This tutorial is intended for people with a background in Data Science or Machine Learning, that want to get up to speed with the field of forecasting. It also covers many aspects of how Machine Learning methods can be used for forecasting, so that it will also have sections of value to forecasters that want to understand better some of the novel techniques that have been successful recently in forecasting competitions.


Tutorial recording


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Tutorial Outline

  1. Introduction and Motivation
    • Good and bad forecasting problems
    • What can we forecast?
  2. Traditional (statistical univariate) forecasting techniques
    • Naive and Mean forecast
    • (Simple) Exponential Smoothing
    • ARIMA
  3. A brief history of forecasting competitions
    • The M competitions (M1, M3, M4, M5)
    • Controversy of Machine Learning vs Statistical methods
    • CIF 2016 competition
  4. Global forecasting models
    • Paradigm shift from local (per-time-series) to global (across-time-series) forecasting models
    • Recent theoretical insights
    • History of global models and Kaggle forecasting competitions
  5. Machine Learning methods for forecasting
    • How to address non-stationarity?
    • Differencing
    • Modelling trend: Detrending, Box-Cox transform, window-wise normalisation
    • Modelling seasonality: Seasonal dummies, Fourier terms, seasonal decompositions, deseasonalisation
    • Normalisation
    • Direct vs iterative predictions
    • Feature engineering
  6. Deep learning for forecasting
    • Recurrent neural networks
    • Convolutional Networks: Causal convolutions, dilations
    • Specialised architectures
    • Multivariate methods
  7. Forecast evaluation
    • Errors and error measures: Scale-free errors, scaled errors, percentage errors, relative errors
    • Fixed and rolling origin evaluation, cross-validation, tests for serial correlation in the residuals
  8. Probabilistic forecasting
    • Analytical prediction intervals
    • Bootstrapping, MCMC sampling
    • Forecasting parameters of distributions
    • Quantile regression (pinball loss)
  9. Special forecasting problems
    • Intermittent time series: zero-inflated models, adapted loss functions
    • Hierarchical time series: classic approaches, optimal reconciliation, probabilistic hierarchical forecasting
  10. Conclusions


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