FractalNet-LSTM Model for Time Series Forecasting  

作　　者：Nataliya Shakhovska Volodymyr Shymanskyi Maksym Prymachenko 

机构地区：[1]Department of Artificial Intelligence,Lviv Polytechnic National University,Lviv,79905,Ukraine

出　　处：《Computers, Materials & Continua》2025年第3期4469-4484,共16页计算机、材料和连续体(英文)

摘　　要：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.

关 键 词：Time series fractal neural networks forecasting LSTM FractalNet 

分 类 号：G63[文化科学—教育学]