A Hybrid Type-2 Fuzzy-LSTM Model for Prediction of Environmental Temporal Patterns

Subject Areas : International Journal of Decision Intelligence

Aref  Safari ¹ (Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran)
Rahil  Hosseini ² (Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran)

Received: 2023-06-04 Accepted : 2023-09-18 Published : 2023-10-01

Keywords: deep learning, Type-2 Fuzzy Logic, Time-Series Prediction, LSTM network,

Abstract :

Computational intelligence methods, such as fuzzy logic and deep neural networks, are robust models to solve real-world problems. In many dynamic and complex problems, statistical attributes frequently change over the time. Recurrent neural networks (RNN) are suitable to model dynamic high-dimensional and non-linear state-space systems. Nevertheless, the RNN is incapable of modelling long-term dependencies in temporal data, and its learning using gradient descent is a complex and difficult task. Long Short-Term Memory (LSTM) networks were introduced to overcome the RNN issues, but coping with uncertainty is still a major challenge for the LSTM models. This research presents a Hybrid Type-2 Fuzzy LSTM (HHT2FLSTM) deep approach to learn long-term dependencies in order to obtain a reliable prediction in uncertain time series circumstances. The proposed model was applied to the air quality prediction problem to evaluate the model’s robustness in handling uncertainties in a real-world application. The proposed model has been evaluated on a real dataset that contains the outdoor pollutants from July 2011 to October 2020 in Tehran and Beijing by a 10-fold cv with an average area under the ROC curve of 97 % with a 95% confidence interval [95-97] %.