One HTTP service, two model families, zero ceremony.
- Foundation time-series — 5 zero-shot forecasters (chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m). Hand them a context window, get quantile or sample-path forecasts. No training step. Six modality-specific endpoints under
/v1/timeseries/<type>/. - Tabular ML — 9 supervised learners (lightgbm, xgboost, hist-gbt, random-forest, logistic, mlp, svm-rbf, knn, naive-bayes) + 3 meta-learners (calibrated, stacking, diversified). Train on YOUR engineered features, persist server-side by
modelId, forecast on the latest snapshot. Under/v1/tabular/. - MCP — streamable-HTTP tools at
/mcp. One named tool per (FM type, model) cell plus per-type ensemble + listing. Tabular endpoints are HTTP-only for now.
docker run -d --name predictalot \
-v $HOME/predictalot-models:/models \
-e PREDICTALOT_AUTH_TOKENS=changeme \
-p 8080:8080 \
psyb0t/predictalot:latest
# Zero-shot FM forecast
curl -s http://localhost:8080/v1/timeseries/univariate/forecast \
-H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
-d '{"model":"chronos-2","context":[[10,11,12,13,14,15,16,17,18,19,20]],"config":{"horizon":5}}' | jq
# Train + persist a tabular model on your own features
curl -s http://localhost:8080/v1/tabular/train \
-H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
-d '{"modelId":"my-model","backend":"lightgbm","target":[[100,101,99,...]],
"features":[{"rsi":[55,58,...],"macd":[0.3,0.4,...]}],
"config":{"mode":"direction","horizon":3,"nEstimators":400}}' | jq
# Then forecast on the latest snapshot
curl -s http://localhost:8080/v1/tabular/forecast \
-H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
-d '{"modelId":"my-model","features":[{"rsi":[58],"macd":[0.4]}]}' | jq| Doc | What it covers |
|---|---|
| docs/timeseries.md | Foundation time-series API. All 5 models (capabilities + per-model quirks + what each is recommended for), all 6 forecast types, per-type ensemble with weights + memberOverrides, extra per-call hatch, /models listings. |
| docs/tabular.md | Tabular ML API. All 9 backends (what each is recommended for), 3 modes (direction / value / quantile), tier-1/2/3 config knobs, the 3 meta-learners (calibrated / stacking / diversified), storage layout. |
| docs/mcp.md | MCP streamable-HTTP server: tool naming, args, current scope (FM only). |
| docs/configuration.md | Every PREDICTALOT_* env var. |
| docs/architecture.md | Multi-venv sidecar pattern for sundial, CPU vs CUDA images, multi-stage build. |
| docs/accuracy.md | Benchmark sMAPE + latency on academic + real-world datasets. Honest takeaways including which models lose. |
| docs/errors.md | Error contract: 400 / 401 / 404 / 413 / 422 / 503 shapes. |
CHANGELOG.md tracks per-version changes.
Code: MIT (see LICENSE).
Foundation models retain their upstream licenses — chronos-2 / timesfm-2.5 / toto-1 / sundial-base-128m: Apache 2.0; moirai-2: CC-BY-NC-4.0 (non-commercial). Tabular backends use their upstream licenses — lightgbm / xgboost / scikit-learn: permissive. Review each before commercial use.