Dynamic panel fuzzy time series model and its application to econometric time series

This study proposes a new Fuzzy Time Series (FTS) approach, called as Dynamic Panel Fuzzy Time Series (DPFTS) which combines Dynamic Panel Data Analysis and FTS. The major advantages of proposed approach can be summarized as follows: i) proposed approach is adapted version of traditional fuzzy time series (TFTS) model to time series data having more than two cross-sections such as countries, cities, regions etc, in other words to panel data. Hence, it has the ability of globally determining the fuzzy sets and the fuzzy relations to be represent a large number of time series simultaneously, ii) it can construct FTS models even if time series have very small sample size since panel data are constructed by combining the time and cross-section dimensions. In order to evaluate the performance of proposed approach and compare it with its traditional version, two experiments which compose of a simulation study and a real time examples are conducted. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criteria are chosen for performance comparisons. Experimental results show that proposed approach exhibits considerable good performance in prediction and forecasting. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study introduces a new Fuzzy Time Series approach, DPFTS, which shows good performance in handling time series data with multiple cross-sections and has strong predictive capabilities.