Time-series forecasting exploded in 2023-2025 when foundation models crossed over from NLP. Nixtla's TimeGPT (2023), Google's TimesFM (2024), and Amazon's Chronos showed that a single pretrained model can zero-shot forecast diverse series, rivaling task-specific statistical models like ETS and ARIMA. Yet the Monash benchmark and M-competition lineage (M4, M5) reveal an uncomfortable truth: simple ensembles of statistical methods still win on many univariate tasks. The real battle now is multivariate long-horizon forecasting, where PatchTST and iTransformer compete with state-space models like Mamba.
100,000 time series from diverse domains (finance, demographic, macro, micro, industry, other). Competition ran in 2018. Lower sMAPE/MASE/OWA is better.
Leading models on M4 Competition.
Didn't find the model, metric, or dataset you needed? Tell us in one line. We read every message and reply within 48 hours.
6 datasets tracked for this task.
Other tasks in Time Series.
Still looking for something on Time Series Forecasting? A missing model, a stale score, a benchmark we should cover — drop it here and we'll handle it.
Real humans read every message. We track what people are asking for and prioritize accordingly.