Time DeCode: A Denoising Codebook-Based Model for Time Series Analysis

Time-series models have been widely explored for learning temporal representations from sequential data. However, real-world time series often contain complex noise from measurement errors, stochastic disturbances, and environmental perturbations, which can affect modeling at multiple stages. We identify three interrelated noise dimensions: data-level noise in token representations, objective-level noise in pretraining targets, and inference-level noise in model predictions. To address these issues, we propose Time DeCode, a denoising codebook-based time-series model that connects representation learning and forecasting inference through a learned code space. Specifically, a noise-aware discrete tokenizer separates sparse irregular components from observations and encodes temporal structure into discrete codes. A next multi-code prediction objective learns temporal dependencies by predicting the code indices of future patches rather than their continuous values, reducing reliance on noisy numerical targets. For prediction, code-space prediction calibration aggregates overlapping predictions for the same future position before decoding to improve inference stability. Extensive experiments on forecasting and classification benchmarks demonstrate the effectiveness of Time DeCode, while noise injection evaluations further support its performance and robustness under noisy observations. The code is available at https://anonymous.4open.science/r/Time_Decode/.