Scaling TSDiff Model Size for Cross-Domain Time Series Forecasting Against Conditional Diffusion Models via CRPS

Abstract

Abstract: We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark c

Research Question

How does scaling the model size of TSDiff impact its performance on cross-domain time series forecasting benchmarks (e.g., UCR archive) compared to conditional diffusion models, measured by CRPS (Continuous Ranked Probability Score)?

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