Intelligent Productivity Transformation

Abstract

With the penetration of deep learning technology into forecasting and decision support systems, enterprises have an increasingly urgent need for accurate forecasting of time series data. Especially in fields such as finance, retail, and production, immediate and accurate predictions of market trends are the key to maintaining a competitive advantage. This study aims to address the limitations of traditional time series forecasting methods, such as the difficulty in adapting to the nonlinearity and non-stationarity of the data, through an innovative deep learning framework. The authors propose a Prophet model that combines deep learning with LSTNet and statistics. In this way, they combine the ability of LSTNet to handle complex time dependencies and the flexibility of the Prophet model to handle trends and periodicity. The particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions. Such a strategy not only helps capture long-term dependencies in time series, but also models seasonality and holiday effects well.