README.md

Available tags:

• Document mode: slide, paper, book, article
• Document type: research, application

Ideas:

• feed this repo to an LLM to enable search and summarization

Techniques and modelling

• Modeling: from basic to advanced concepts
  • Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends: a didactic paper on how to deal with time series non-stationarity (and on the consequences of ignoring it)
  • Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
• Cross-validation
  • Experiments on cross-validation (Samuele Mazzanti, inspired by Victor Cerqueira et al)
• Feature-based approaches
  • paper Characteristic-based clustering for time series. Using feature-based approaches to cluster time series greatly reduces the dimensionality of your problem
  • slide Meta-learning on how to forecast time series. FFORMS: what type of models are most suitable for a given time serie?
• Metrics
  • paper Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices. Hewamalage, Ackermann, Bergmeir

Applications

Retail forecasting

• book Demand forecasting best practices (N. Vandeput)
• slide Retail sales forecasting at Walmart (Seaman). Predict for what? Addresses replenishment and pricing issues
• paper Forecasting with trees: why tree-based methods achieved excellent results in the M5 competitions? What are the directions to improve even further?
• paper Learnings from (various) Kaggle forecasting competitions (Bojer and Meldgaard)

Others

• paper Empirically validated probabilistic forecasts of energy technology costs. How to estimate stuff in a regime of technological uncertainty (due to learning effects)?

People, thoughts and organizational aspects

• Sarem Seitz. Featured: when point forecasts fail, autoregressive random forests, on probabilistic forecasts
• Marco Cerliani. Featured: forecasting with trees (hybrid modelling)
• paper retail Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning

Practical tips

This section is not strictly related to time series but collects useful tips in Python, Model deployment and specific libraries

• Tricking data science: time series

Waitlist

1. Monash Time Series Forecasting Archive (arxiv)
2. Modern strategies to time series regression (arxiv)
3. Forecasting for social good (arxiv)
4. FRANS automatic feature extraction via CNN (arxiv)
5. "Our simplified HINT approach shifts away from multivariate inputs, leading to a 13% accuracy increase over existing solutions (such as MinTrace, PERMBU, HierE2E) across four hierarchical datasets" arxiv, nixtla tutorial
6. How to win a forecasting competition? Youtube video
7. paper Good and bad judgment in forecasting, lessons from four companies
8. paper Can we obtain valid benchmarks from published surveys of forecast accuracy? (Kolassa)
9. book Supply Chain Strategy and Financial Metrics: The Supply Chain Triangle Of Service, Cost And Cash on amazon
10. book The Business Forecasting Deal: Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions on Amazon