A New Hybrid Model of Fuzzy Time Series and Genetic Algorithm Based Machine Learning Algorithm: A Case Study of Forecasting Prices of Nine Types of Major Cryptocurrencies

Abstract

This study pertains to the usage and effectiveness of the fuzzy time series (FTS) models and machine learning methods in forecasting movements of financial data. The datasets used in this study are the actual closing prices and transaction volumes of 9 different cryptocurrencies, from the earliest time obtainable in Yahoo! Finance, all the way to 31 Oct 2021. Firstly, this paper presents a study of the severe drawbacks of all existing literature on FTS. In particular, this article outlines severe shortcomings of all existing FTS based algorithms that caused inaccuracies among all existing FTS-based algorithms in yielding meaningful prediction. Then, a novel structure of our improvised FTS, denoted as QFTS, is presented in this paper, which aims to rectify all flaws exist in all conventional FTS based models in literature. A further hybrid of QFTS with ANN is also presented. Later, a comparative analysis of all the aforementioned FTS models is presented in terms of overall forecasting accuracy and forecasting accuracy under specific conditions. The results are being compared in terms of MAPE. The newly invented QFTS model and the QFTS-ANN hybrid is found to profoundly outperform all the existing FTS models in literature, which includes Singh's FTS model. Such innovation profoundly rectifies severe shortcomings in financial forecasting that have persisted for many years in the past literature.