Aggregating Strategies for Long-term Forecasting
This work addresses long-term forecasting challenges in sequential prediction, but it is incremental as it builds on existing aggregating algorithms.
The authors tackled the problem of long-term forecasting by generalizing Vovk's aggregating algorithm and proposing two modifications, one with a time-independent regret bound and another with an O(√T) regret bound for practical use.
The article is devoted to investigating the application of aggregating algorithms to the problem of the long-term forecasting. We examine the classic aggregating algorithms based on the exponential reweighing. For the general Vovk's aggregating algorithm we provide its generalization for the long-term forecasting. For the special basic case of Vovk's algorithm we provide its two modifications for the long-term forecasting. The first one is theoretically close to an optimal algorithm and is based on replication of independent copies. It provides the time-independent regret bound with respect to the best expert in the pool. The second one is not optimal but is more practical and has $O(\sqrt{T})$ regret bound, where $T$ is the length of the game.