Weighted Ensemble of Statistical Models

2018/11/19 by Maciej Pawlikowski, Pawlikowski, Maciej, Agata Chorowska +1 · 3 citations
Computer Science · Decision Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Forecasting Techniques and Applications #Stock Market Forecasting Methods #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1811.07761

openalex publication_date 2018/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a detailed description of our submission for the M4 forecasting competition, in which it ranked 3rd overall. Our solution utilizes several commonly used statistical models, which are weighted according to their performance on historical data. We cluster series within each type of frequency with respect to the existence of trend and seasonality. Every class of series is assigned a different set of models to combine. Combination weights are chosen separately for each series. We conduct experiments with a holdout set to manually pick pools of models that perform best for a given series type, as well as to choose the combination approaches.

Citations

• The theta model: a decomposition approach to forecasting

Cited by

• Multi-layer Stack Ensembles for Time Series Forecasting
• Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting
• The cost of ensembling: is it always worth combining?

Related

• Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation
• Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach
• Improving forecasting by subsampling seasonal time series
• Attention-Based Ensemble Pooling for Time Series Forecasting
• Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting