SSDNet: State Space Decomposition Neural Network for Time Series Forecasting
This addresses the problem of accurate and interpretable forecasting for time series analysis, with incremental improvements in method integration.
The paper tackles time series forecasting by introducing SSDNet, which combines Transformer architecture with state space models to provide probabilistic and interpretable forecasts, and shows it outperforms state-of-the-art methods in accuracy and speed on five datasets.
In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probabilistic and interpretable forecasts, including trend and seasonality components and previous time steps important for the prediction. The Transformer architecture is used to learn the temporal patterns and estimate the parameters of the state space model directly and efficiently, without the need for Kalman filters. We comprehensively evaluate the performance of SSDNet on five data sets, showing that SSDNet is an effective method in terms of accuracy and speed, outperforming state-of-the-art deep learning and statistical methods, and able to provide meaningful trend and seasonality components.