Projects per year
We propose an automated method for obtaining weighted forecast combinations using time series features. The proposed approach involves two phases. First, we use a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination. The inputs to the meta-model are features that are extracted from each series. Then, in the second phase, we forecast new series using a weighted forecast combination, where the weights are obtained from our previously trained meta-model. Our method outperforms a simple forecast combination, as well as all of the most popular individual methods in the time series forecasting literature. The approach achieved second position in the M4 competition.
|Number of pages||7|
|Journal||International Journal of Forecasting|
|Publication status||Published - Jan 2020|
- Forecast combination
- M4 competition
- Time series features
- 1 Finished
Macroeconomic forecasting in a 'Big Data' world
Panagiotelis, A., Athanasopoulos, G., Hyndman, R. & Vahid-Araghi, F.
Australian Research Council (ARC)
1/08/14 → 31/07/19