INTEGRATION OF FUZZY LOGIC INTO THE TimeGAN GENERATIVE ADVERSARIAL NETWORK FOR FINANCIAL TIME SERIES FORECASTING
DOI:
https://doi.org/10.34185/1991-7848.itmm.2026.01.085Keywords:
TimeGAN, fuzzy logic, Mamdani system, adaptive window, generative adversarial networks, time series forecastingAbstract
The problem of a fixed observation window length in the TimeGAN generative adversarial network is examined, and a method for resolving it through the integration of a fuzzy controller for parameter k is proposed. Unlike deterministic hyperparameter tuning, the fuzzy module Φ: S → K = {10,…,60} dynamically adapts k according to the current market regime, characterized by a vector of four indicators: market volatility, the normalized slope of a linear regression, the normalized trading volume relative to the moving average, and news sentiment volatility. The architectural modifications to the TimeGAN embedding function required for variable window integration are described, and the effectiveness of the approach is confirmed on two assets (S&P 500, AAPL) over the period 2019–2025: MAE decreased by 16,4% for S&P 500 and by 12,9% for AAPL compared to the baseline TimeGAN with fixed k.
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