A Complex Fuzzy LSTM Network for Temporal-Related Forecasting Problems

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy on such type of time-series data, this study proposes integrating deep learning approaches with fuzzy logic. Particularly, it combines the long short-term memory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model (CFLSTM). The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory (LSTM) technique to have greater power for processing time series data. Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposed model’s utility compared to other well-known forecasting models. The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.