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 methods based on real-world compressors. We consider predicting univariate multivariate data, describe how multiple compressors can be combined into one method auto...

We consider forecasting functional time series of extreme values within a generalized value distribution (GEV). The GEV can be characterized using the three parameters (location, scale, and shape). As result, forecasts density accomplished by these latent parameters. Depending on underlying data structure, some either modeled as scalars or functions. provide two algorithms to model forecast To ...

Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming...