  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.
