Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting

Understand

Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches.

• While longer sequences inherently offer richer information for enhanced predictive precision, prevailing studies often respond by escalating model complexity.
• These intricate models can inflate into millions of parameters, resulting in prohibitive parameter scales.
• Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation while achieving uniformly superior and robust results across various datasets.