A Novel Forecasting Method Based on F-Transform and Fuzzy Time Series

Abstract

The main goal of time series analysis is to establish forecasting model based on past observations and to reduce forecasting error. To achieve these goals, the present paper proposes a new forecasting algorithm based on the fuzzy transform (F-transform) and the fuzzy logical relationships. First, the F-transform is performed based on partitioning of the universe, and the fuzzy logical relationships are employed to forecast. Two experimental applications are used to illustrate and verify the proposed algorithm. The accuracies are evaluated on the basis of average forecasting error percentage and index of agreement to compare the proposed algorithm with other existing methods.