Kairos: Towards Adaptive and Generalizable Time Series Foundation Models

Time series foundation models (TSFMs) have emerged as a powerful paradigm for time series analysis, driven by large-scale pretraining on diverse data corpora. However, time series inherently exhibit heterogeneous information density over time, influenced by system states and signal complexity, presenting significant modeling challenges especially in a zero-shot scenario. Current TSFMs rely on non-adaptive processing pipelines that fail to capture this dynamic nature. For example, common tokenization strategies such as fixed-size patching enforce rigid observational granularity, limiting their ability to adapt to varying information densities. Similarly, conventional positional encodings impose a uniform temporal scale, making it difficult to model diverse periodicities and trends across series. To overcome these limitations, we propose Kairos, a flexible TSFM framework that integrates a dynamic patching tokenizer and an instance-adaptive positional embedding. Kairos adaptively selects tokenization granularity and tailors positional encodings to the unique characteristics of each time series instance. Trained on a large-scale Predictability-Stratified Time Series (PreSTS) corpus comprising over 300 billion time points and adopting a multi-patch prediction strategy in the inference stage, Kairos achieves superior performance with much fewer parameters on two common zero-shot benchmarks, GIFT-Eval and the Time-Series-Library benchmark, consistently outperforming established methods across diverse tasks.

The Kairos architecture consists of three main components. First, the input time series is tokenized by a Mixture-of-Size Dynamic Patching (MoS-DP) module to extract multi-granularity local information. These embeddings are then processed by a Transformer encoder, which uses our proposed Instance-Adaptive Rotary Position Embedding (IAROPE) to model complex temporal relationships. Finally, a Transformer decoder utilizes a multi-patch prediction strategy for forecasting.

Kairos demonstrates remarkable zero-shot forecasting capabilities, outperforming both recent advanced TSFMs and the majority of full-shot deep learning models on the TSLib benchmark. The charts below show the aggregate average Mean Absolute Error (MAE) and Mean Squared Error (MSE), where lower values indicate better performance.