activity
20192022
most citedMachine learning applications in time series hierarchical forecasting

14 citations · 46 across the 12 of their papers we have counts for

collaborators

19 papers

cs.LG2022

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…

cs.LG2022

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…

cs.LG20227 cited

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…

cs.LG2022

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…

cs.LG20212 cited

A Look at the Evaluation Setup of the M5 Forecasting Competition

Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir +1

Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision…

stat.AP202114 cited

MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns

Kasun Bandara, Rob J Hyndman, Christoph Bergmeir

The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates lea…