Time series models forecast demand, sales, service load and inventory from historical data. Some work without lengthy training on your data. When choosing, consider the forecast horizon, handling of seasonality and external factors, the license and hardware needs.
A ready-made Google forecasting model: forecasts any time series without training on your data.
A compact forecasting model on the xLSTM architecture, a leader in open benchmarks despite its small size. Runs fast on a regular CPU.
A compact forecasting foundation model from the Tsinghua lab: trained on a large set of diverse series and fine-tunable on your own data.
A Datadog forecasting model trained on server and application metrics. Especially strong for IT monitoring: load, latency, errors.
Amazon forecasting models, among the most downloaded. Chronos-2 takes external factors into account: prices, promotions, weather.
Salesforce's universal forecasting model for series with different frequencies and many variables. Weights are open for research only.
A foundation model for market candlestick data: trained on data from more than 45 exchanges, it forecasts prices and volumes. The largest version, large, is not open.
A forecasting model that returns a set of possible scenarios rather than a single line — useful when you need a range for demand or load, not one number.
A foundation model for numeric series: one engine is used for forecasting, anomaly detection, filling gaps and classification.
Tiny forecasting models from IBM: they run on an ordinary CPU and sit next to the business system without a separate GPU server.
A forecasting model with a sparse architecture: only part of the network runs at each step, so it stays fast at a small size.
One of the first open out-of-the-box forecasting models. Tiny, gives a probabilistic forecast, now behind Chronos and TimesFM.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
