Fuzzy Time Series Forecasting Approach using LSTM Model

Authors

  • Radha Mohan Pattanayak Pattanayak Godavari Institute of Engineering and Technology
  • M. V. Sangameswar Godavari Institute of Engineering and Technology
  • Deepika Vodnala Vignana Bharathi Institute of Technology
  • Himansu Das KIIT Deemed to be University

DOI:

https://doi.org/10.13053/cys-26-1-4192

Keywords:

Long short term memory (LSTM), fuzzy time series forecasting (FTSF), fuzzy logical relationships (FLRs), length of interval (LOI), number of Interval (NOI), time series (TS), fuzzy set theory (FST)

Abstract

In the present scenario, fuzzy time series forecasting (FTSF) is an interesting concept by the researchers to approach the uncertainty in the dataset. In the current study, we proposed a fuzzy long short term memory (FLSTM) model to forecast a wide range of time series (TS) dataset with less computational complexity. The present research mainly focuses on two issues such as 1) in order to obtain the number of intervals (NOIs) of the universe of discourse (UOD) the trend based discretization (TBD) approach is applied, and 2) the subscript of the fuzzy set associated with the crisp observation is considered to establish the fuzzy logical relationships (FLRs) for the proposed FLSTM model. To demonstrate the forecasting ability of the FLSTM model, six TS datasets with three profound FTSF models are considered. The empirical result analysis revealed that, in all measured the proposed model outperformed and showed better result than its alternatives. The outcome of the different FTSF models on different measures proves the outperformance of the FLSTM model than its competitors.

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Published

2022-03-27

Issue

Vol. 26 No. 1 (2022): Recent Advances in Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez, D. Vilariño)

Section

Articles

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