The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and methods. This paper presents TSPred, a framework for prediction. It differs from the mainstream frameworks establishes process that seamlessly integrates transformations with state-of-the-art statistical machine learning made available as an R-package, which provides functions defi...