Time Series Prediction Based on the Probability Diffusion Model

Time series forecasting plays a crucial role in practical applications such as extreme weather warning and long-term energy consumption planning. However, accurately predicting long-term trends in time series data is a challenging problem. In the past, models based on the Transformer architecture have been used to discover long-distance dependencies through various self-attention mechanisms, but the complexity of long-term time patterns has hindered the discovery of reliable dependencies. Moreover, Transformers must use a sparse version of self-attention on a per-point basis to improve efficiency with long sequences, which creates an information bottleneck. To address this challenge, we developed a model called ST:DDP that utilizes probability diffusion models and automatic correlation mechanisms to encode and decode time series data. The diffusion model, which is the most advanced generative model, and the structured state space model, as the internal model architecture, are particularly suitable for capturing long-term dependencies in time series data. Compared to self-attention models, the ST:DDP model has advantages in both efficiency and accuracy. In terms of long-term prediction, we tested the ST:DDP model on open-source data and achieved state-of-the-art accuracy. Our approach builds on the theory of random processes to provide a new, more effective method for long-term time series forecasting, with implications for a range of practical applications.