Abstract
Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathematically. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.
Highlights
The problem of time series forecasting is to estimate the future values of a process from a sequence of its observations
We propose an adaptive approach to time series forecasting, which is useful in situations when we do not know in advance which data compressor is optimal for a given time series
The only issue is computation time—we need to compress all sequences with every data compression algorithm that we include in our combination
Summary
The problem of time series forecasting is to estimate the future values of a process from a sequence of its observations. This task is important because it has many practical applications. There are many different approaches to solving this problem. Classical statistical models such as exponential smoothing and the autoregressive integrated moving average (ARIMA) model are very popular, highly accurate, and relatively easy to use. A detailed description of these methods can be found in [1,2]. There is no best method for all situations, and the development of new forecasting techniques remains relevant
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