Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
This work addresses forecasting accuracy for domains with complex seasonal patterns, but it is incremental as it applies existing meta-learning methods to a specific problem.
The paper tackles the problem of improving forecast accuracy for time series with complex seasonality by using meta-learning to combine forecasts from different models, demonstrating superior performance compared to simple averaging.
In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging.