Dynamic Ensemble Methods for Time Series Forecasting
Current Work
Both complex and evolving nature of time series structure
make forecasting among one of the most important and challenging tasks
in time series analysis. Typical methods for forecasting are designed to
model time-evolving dependencies between data observations. However,
it is generally accepted that none of them is universally valid for every
application. Therefore, methods for learning heterogeneous ensembles
by combining a diverse set of forecasts together appear as a promising
solution to tackle this task. Hitherto, in classical ML literature, ensemble
techniques such as stacking, cascading and voting are mostly restricted
to operate in a static manner. To deal with changes in the relative
performance of models as well as changes in the data distribution, we
propose a drift-aware meta-learning approach for adaptively selecting
and combining forecasting models. Our assumption is that different
forecasting models have different areas of expertise and a varying relative
performance. Our method ensures dynamic selection of initial ensemble
base models candidates through a performance drift detection mechanism.
Since diversity is a fundamental component in ensemble methods, we
propose a second stage selection with clustering that is computed after
each drift detection. Predictions of final selected models are combined
into a single prediction. An exhaustive empirical testing of the method
was performed, evaluating both generalization error and scalability of the
approach using time series from several real world domains. Empirical
results show the competitiveness of the method in comparison to state-
of-the-art approaches for combining forecasters.
DEMSC Software
More details can be found in: https://github.com/AmalSd/DEMSC
Future Work
- Add theoretical insights
- Generalise to all the types of data streams
Projects
SFB 876
Software
A Drift-based Dynamic Ensemble Members Selection using Clustering For Time Series Forecasting
Staff
Saadallah, Amal
