A different approach on fuzzy time series forecasting model

Vamitha, V.

doi:10.1016/j.matpr.2020.04.579

Cited by 2 publications

(1 citation statement)

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“…For example, Pal and Kar's research [15] used Lee's model to predict the RSME values for the BSE, NYSE, and TAIEX stock exchanges, 136.04, 66.85, and 62.57, respectively. Using Lee's Fuzzy Time Series approach, Vamitha [16] predicted temperature data and obtained an AFER value of 1.21%. In research on natural gas price forecasting, Dodi et al [17] found that Lee's model, the Fuzzy Time Series approach, had a lower error rate (MAPE value of 6.885%) than Chen's model.…”

Section: Introductionmentioning

confidence: 99%

Implementing lee's model to apply fuzzy time series in forecasting bitcoin price

Farida,

Ainiyah

2024

Comput. Sci. Inf. Technol.

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Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.

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Section: Introductionmentioning

confidence: 99%