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Dealing with missing data using attention and latent space regularization
Jahan C. Penny-Dimri, Christoph Bergmeir, Julian Smith
Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating proce…
Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
Ankitha Nandipura Prasanna, Priscila Grecov, Angela Dieyu Weng +1
The electricity industry is heavily implementing smart grid technologies to improve reliability, availability, security, and efficiency. This implementation needs technological adv…
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL technique…
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
Dilini Rajapaksha, Christoph Bergmeir
Accurate electricity demand forecasts play a crucial role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is ne…