2025 Forecasting For Data Scientists Free Course

16 December 2025

Course description

In this course, we will briefly recap the history of the field of forecasting and its developments parallel to machine learning, look into the basics and classical methods in forecasting, with a focus on how they are still relevant today. We will then look into recent developments in the field, around learning across series with global models, traditional machine learning methods such as gradient boosted trees, and how they can be used for forecasting. We then go into deep-learning with recurrent neural networks (RNNs), CNNs, and transformer models, before we look into foundation models and the newest developments like prior-fitted networks and agentic forecasting. We discuss that there is no one size fits all, and that different forecasting problems will require different solutions, which is still true today in the age of foundation models. We further will look into the intricacies of forecast evaluation, and into specialised topics such as multivariate forecasting, intermittent time series, hierarchical forecasting, and others.

Intended audience

This course is intended for practitioners and academics with a background in Data Science or Machine Learning that want to get up to speed in the field of forecasting.

FULL RECORDINGS ON YOUTUBE

Full course playlist - Youtube

Slides

 FFDS Course Part 1 pdf  FFDS Course Part 2 pdf  FFDS Course Part 3 pdf


Course Outline

This course targets practitioners and researchers with a Machine Learning and Data Science background that want to skill up in forecasting. It has a total of about 6 hours of content and consists of four parts:

Part 1: Introduction and traditional statistical models

  • 1.01 Who am I, what is this course about?
  • 1.02 What do we forecast? What can and can’t we forecast?
  • 1.03 Terminology and simple techniques: mean forecast, naive, random walk, Simple Exponential Smoothing (SES)
  • 1.04 Forecasts are always probabilistic!, loss functions, definition of stationarity
  • 1.05 Trend and seasonality decomposition, autocorrelation, ETS
  • 1.06 Linear models, ARIMA, differencing
  • 1.07 Transformations: logarithm, Box-Cox
  • 1.08 Model selection (AIC, BIC), AutoETS, AutoARIMA
  • 1.09 TBATS, Prophet
  • 1.10 A brief history of forecasting competitions (M, M3, M4, CIF2016), conclusions FFDS part 1

Part 2: Machine Learning methods for forecasting

  • 2.01 Global models: Global model theoretical guarantees, global modelling works even if series are not related, Global models are not multivariate models, history of global models, history of Kaggle competitions, M5 competition
  • 2.02 Traditional Machine learning methods: How to deal with non-stationarity? More differencing, and other methods
  • 2.03 Trend modelling, window-wise normalisation
  • 2.04 How to model seasonality? Fourier terms, deseasonalisation
  • 2.05 Normalisation
  • 2.06 Direct vs iterative modelling, GBTs, feature engineering, ensembling and forecast combination, software
  • 2.07 Deep learning for forecasting: RNNs, CNNs
  • 2.08 Transformers for forecasting, some problems, flawed evaluations
  • 2.09 Multivariate forecasting with deep-learning methods, software for deep-learning for forecasting, data repositories and benchmarks
  • 2.10 Foundation models for forecasting, some problems and how to use them properly
  • 2.11 Newest developments: PFNs, agentic forecasting, multimodal models, conclusions FFDS part 2

Part 3: Evaluation and Probabilistic Forecasting

  • 3.01 Data partitioning and model training, fixed- vs rolling origin, cross-validation, data leakage
  • 3.02 Bias, variance, RMSE, MAE, general considerations for scale-free measures
  • 3.03 Percentage errors, point forecasts are summary statistics over a forecast distribution, MAPE, sMAPE, MAAPE, RelMAE
  • 3.04 Calculating error measures per series and across series, WAPE/WRMSPE
  • 3.05 MASE/RMSSE, Summary and recommendations for error measures
  • 3.06 Probabilistic forecasting: Use analytical prediction intervals, simulation and bootstrapping, quantile regression, MCMC sampling, …
  • 3.07 Evaluation of probabilistic forecasts, conclusions FFDS part 3

Part 4: Some special forecasting topics

  • 4.01 External regressors
  • 4.02 Intermittent series
  • TBC Hierarchical forecasting
  • TBC Forecasting in retail
  • TBC Interpretability
  • TBC Forecast Stability
  • TBC Causal Inference
  • TBC Conclusions FFDS part 4

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