Open Access

ARTICLE

FractalNet-LSTM Model for Time Series Forecasting

Nataliya Shakhovska, Volodymyr Shymanskyi^*, Maksym Prymachenko

Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, 79905, Ukraine

* Corresponding Author: Volodymyr Shymanskyi. Email:

Computers, Materials & Continua 2025, 82(3), 4469-4484. https://doi.org/10.32604/cmc.2025.062675

Received 24 December 2024; Accepted 07 February 2025; Issue published 06 March 2025

Abstract

Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets of electricity consumption, sunspot activity, and Spotify stock price. The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies. However, for financial data with high volatility, the model’s efficiency decreases at long forecasting horizons, indicating the need for further adaptation. The findings suggest further adaptation. The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.

---

Keywords

---