Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19

Abstract

Major hyperparameters which affect fuzzy time series (FTS) forecasting are the number of partitions, length of partition intervals in the universe of discourse, and the fuzzy order. There are very few studies which have considered an integrated solution to optimize all the hyperparameters. In this paper, we strive to achieve optimum values of all three hyperparameters for fuzzy time series forecasting of the COVID-19 pandemic using the Particle Swarm Optimization (PSO) algorithm. We specifically propose two techniques, namely nested FTS-PSO and exhaustive search FTS-PSO for determining the optimal interval length, as an augmentation to the FTS-PSO model that optimizes the interval length and the fuzzy order. Nested PSO has two PSO loops: (i) the inner PSO optimizes the combination of fuzzy order and boundaries of intervals for a given number of partitions defined by the outer loop, and the resultant cost is fed back to the outer PSO; (ii) the outer PSO optimizes the number of partitions to reduce the cost while meeting the defined constraint. Exhaustive search FTS-PSO also has two loops where the inner loop is similar to nested FTS-PSO while the outer loop iterates over a pre-defined search space of number of partitions. We analyze the effectiveness of the two approaches by comparing with ARIMA, FbProphet, and the state-of-the-art FTS and FTS-PSO models. We adopt COVID-19 highly affected 10 countries worldwide to perform forecasting of coronavirus confirmed cases. We consider two phases of COVID-19 spread, one from the year 2020 and another from 2021. Our study provides an analytical aspect of the COVID-19 pandemic, and aims to achieve optimal number and length of intervals along with fuzzy order for FTS forecasting of COVID-19. The results prove that the exhaustive search FTS-PSO outperformed all the methods whereas nested FTS-PSO performed moderately well.

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

“…Ceylan ( 2021 ) proposed a hybrid model based on GM(1,1) and a PSO and utilized it to forecast the cumulative case number of COVID-19 in Germany, Turkey, and USA. Kumar and Susan ( 2021 ) considered fuzzy time-series and PSO to forecast the COVID-19 pandemic. Li et al ( 2021 ) identified new infected COVID-19 patients in terms of small data and poor information using the improved GM(1,1) model.…”

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

“… [20] evoked the COVID-19 impact on ambulances’ turnaround time and proposed a two-stage machine learning methodology to solve the problem in a given time and hospital. Kumar and Susan [21] proposed a novel fuzzy time series forecasting with the particle swarm optimization to handle the emergency ambulance dispatch problem.…”

A Study for Plausible Third Wave of COVID-19 in India through Fuzzy Time Series Modelling Based on Particle Swarm Optimization and Fuzzy c-Means

“…(5) end for (6) assign linguistic variable to all historical data according to the belonging of data to their respective subinterval (7) define FLR and FLRG (8) defuzzify the historical data by using (21) for known linguistic variable (9) determine the forecasted value by using (22) for unknown linguistic variable (10) return forecasted values Both phases I and II are depicted through flow chart in Figure 1.…”

“…Mahmoudi et al [7] compared the COVID-19 spread rate in high-risk countries using time series and fuzzy clustering algorithms. Kumar and Susan [8] proposed two approaches, named as, nested FTS-PSO and exhaustive search FTS-PSO to forecast the COVID-19. Elleuch et al [9] proposed method for real time forecasting of COVID-19 patients health based on artificial neural networks and fuzzy interval mathematical modeling.…”