A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications

Abstract

An accurate long-term forecasting for some time series in industrial production is substantially significant for improving the economic efficiency of industry enterprise. In this study, a granular computing (GrC)-based deep learning framework is proposed for long-term time series forecasting, which consists of two stages i.e., the adaptive data granulation and the GrC-based deep model construction. In the first stage, for automatically generating the basic information granules with unequal time span adaptively from data, a stacked sparse auto-encoders granulation network is designed, in which the starting and ending points of a granule are adaptively determined by setting a single neuron in the last hidden layer after multi-layer feature extraction. Then, the second stage sees a definition of a partially overlapping sub-block basis (POSB) matrix to extract the features of these granules, based on which a deep sparse coding feature decomposition-based long-term forecasting model is constructed to transform the unequal-length granules into a product of a POSB matrix and a coefficient matrix layer by layer to serve for forecasting. To verify the effectiveness of the proposed method, two synthetic datasets, two real-world datasets and two practical industrial datasets are employed. The experimental results demonstrate that the proposed method outperforms other data-driven ones on long-term time series forecasting, particularly in an industrial case.