Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting
Stock price forecasting is hindered by the heteroscedastic nature of financial time series, in which turbulent and tranquil regimes alternate. Although the Long Short-Term Memory (LSTM) network is widely used for this task, its gates are oblivious to such volatility variation, so memory retention remains fixed regardless of market conditions. A common remedy is to supply volatility estimates as an additional input feature, yet this leaves the internal memory dynamics untouched. Instead, volatility can serve as a direct regulatory signal over the gate activation itself. This paper presents the Gsigmoid-LSTM model, which embeds Generalized Autoregressive Conditional Heteroscedasticity (GARC...
| Authors | Xin-Rui Lu, Da-Qing Zhang |
|---|---|
| Primary category | cs.CE |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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