A novel hybrid deep learning time series forecasting model based on long-short-term patterns

Time series forecasting constitutes a cornerstone in decision-making processes across diverse domains, ranging from finance and economics to healthcare and environmental science. However, traditional long-term forecasting models often overlook the nuanced characteristics of short-term patterns, which exhibit rapid fluctuations within brief intervals. These short-term patterns, encapsulating frequency domain features, encode valuable information about transient phenomena and periodic variations that can significantly impact predictive accuracy. In the paper has been proposed a novel approach to long-term forecasting of multi-dimensional time series data using convolutional and recurrent neural networks. The proposed model harnesses the power of advanced neural network architectures, integrating both short and long-term time series patterns trends while concurrently leveraging Fourier transform analysis to extract frequency domain patterns. By synthesizing information from short-term fluctuations and long-term trends, the proposed model offers a comprehensive framework for robust forecasting, enhancing predictive accuracy across diverse applications. Through extensive simulations, the results of experiments demonstrate the superior performance of proposed model compared to conventional forecasting techniques, thereby showcasing its potential to revolutionize time series forecasting methodologies.