  In this study we show that standard well-known file compression programs
(zlib, bzip2, etc.) are able to forecast real-world time series data well. The
strength of our approach is its ability to use a set of data compression
algorithms and "automatically" choose the best one of them during the process
of forecasting. Besides, modern data-compressors are able to find many kinds of
latent regularities using some methods of artificial intelligence (for example,
some data-compressors are based on finding the smallest formal grammar that
describes the time series). Thus, our approach makes it possible to apply some
particular methods of artificial intelligence for time-series forecasting.
  As examples of the application of the proposed method, we made forecasts for
the monthly T-index and the Kp-index time series using standard compressors. In
both cases, we used the Mean Absolute Error (MAE) as an accuracy measure.
  For the monthly T-index time series, we made 18 forecasts beyond the
available data for each month since January 2011 to July 2017. We show that, in
comparison with the forecasts made by the Australian Bureau of Meteorology, our
method more accurately predicts one value ahead.
  The Kp-index time series consists of 3-hour values ranging from 0 to 9. For
each day from February 4, 2018 to March 28, 2018, we made forecasts for 24
values ahead. We compared our forecasts with the forecasts made by the Space
Weather Prediction Center (SWPC). The results showed that the accuracy of our
method is similar to the accuracy of the SWPC's method. As in the previous
case, we also obtained more accurate one-step forecasts.
