- Forecasting at Scale (Facebook Prophet)
- Gaussian Processes for Timeseries Modelling
- Neural forecasting: Introduction and Literature Overview, Amazon Research
- DeepAR: Probabilistic forecasting with autoregressive recurrent networks
- Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- Applied Time Series Analysis for Fisheries and Environmental Sciences, E. E. Holmes, M. D. Scheuerell, and E. J. Ward
- Forecasting: Principles and Practice, Rob J Hyndman and George Athanasopoulos
- Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams
- Introduction to Algorithmic Marketing, Ilya Katsov
- Introduction to Time Series and Forecasting, Peter J. Brockwell and Richard A. Davis
- Little Book of R for Time Series, Avril Coghlan
- Statistical Rethinking, Richard McElreath
- Time Series Analysis and its Applications: with R Examples, Robert H. Shumway and David S. Stoffer
- Facebook Prophet - Docs
- stumpy - Used to efficiently compute the matrix profile.
- 100 Time Series Data Mining Questions (with answers!)
- https://petolau.github.io - Many interesting posts around time series analysis.
- Exploring TensorFlow Probability STS Forecasting
- Forecasting Weekly Data with Prophet
- Intro CausalImpact
- Structural Time Series modeling in TensorFlow Probability
- Time Series Analysis and Forecasting with ARIMA (Python)
- atspy - Automated Time Series Models in Python.
- CausalImpact (Google)
- GluonTS - Probabilistic Time Series Modeling
- deep-learning-with-python-notebooks
- deep-learning-with-r-notebooks
- tensor-house (Introduction to Algorithmic Marketing)
- SHAP
- sktime- Python toolbox for machine learning with time series compatible with scikit-learn.
- tsfresh - Time Series Feature extraction based on scalable hypothesis tests.
- Gaussian Processes for Time Series Forecasting, Second Symposium on Machine Learning and Dynamical Systems @Fields Institute, Juan Orduz