Time-Series Foundation Models for Zero-Shot Forecasting

Abstract

Forecasting pipelines have long relied on a model trained separately for every dataset, an approach that is expensive to maintain and slow to adapt when new series arrive. Time-series foundation models change this picture. A single network is pretrained once on a very large and varied collection of series, after which it produces forecasts on previously unseen data without any further fitting. This paper surveys the design and evaluation of such models. The series is first split into fixed-length patches that act as tokens, and a Transformer backbone, either decoder-only or encoder-based, predicts future patches. The pretraining objective, tokenization scheme, and probabilistic output head together determine zero-shot quality. Five representative systems are examined, namely TimesFM, Chronos, Moirai, Lag-Llama, and TimeGPT, and their inductive choices are contrasted. Using illustrative metrics aligned with reported behaviour on the Monash archive and GIFT-Eval, the study compares these models against classical baselines such as ARIMA and exponential smoothing and against trained deep networks including N-BEATS, PatchTST, and DeepAR. The evidence indicates that a frozen foundation model often matches a per-dataset deep model while removing training cost at deployment, though a tuned specialist still leads on some series. Open problems remain around external covariates, very long horizons, and distribution shift, and the paper outlines directions that address them. The intent is descriptive synthesis rather than a single deployed benchmark.