predictalot

Self-hosted forecasting/prediction service. Foundation time-series endpoints under /v1/timeseries/<type>/{forecast,forecast/ensemble} + GET .../models — univariate, past/future/both covariates, multivariate, samples — over 5 zero-shot models (chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m). Plus supervised tabular ML under /v1/tabular/ (9 backends — lightgbm, xgboost, hist-gbt, random-forest, logistic, mlp, svm-rbf, knn, naive-bayes — over direction/value/quantile modes) with train+persist, weighted ensembles, and calibrated/stacking/diversified meta-learners. Unified REST + MCP (streamable-HTTP at /mcp, one tool per (type, model) cell + per-type ensemble + listing) + optional bearer auth. Use when the user wants to forecast a numeric time series (quantile bands or raw sample paths), condition a forecast on known/future covariates, ensemble several forecasters, or train a tabular model on engineered features and predict direction/value/quantiles on the latest snapshot.

Self-hosted forecasting service — one HTTP container, two model families.

• Foundation time-series (zero-shot) — 5 forecasters (chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m). Hand them a context window, get quantile bands or raw sample paths. No training step. Routed by forecast type under /v1/timeseries/<type>/ — univariate, covariates/past, covariates/future, covariates (past+future), multivariate, samples. Each type has forecast, forecast/ensemble, and a models listing.
• Tabular ML (supervised) — 9 learners (lightgbm, xgboost, hist-gbt, random-forest, logistic, mlp, svm-rbf, knn, naive-bayes), all supporting 3 modes (direction / value / quantile). Train on YOUR engineered features, persist server-side by modelId, forecast on the latest feature snapshot. Weighted ensembles over stored models plus 3 meta-learners (calibrated / stacking / diversified). Under /v1/tabular/.
• MCP — streamable-HTTP tools at /mcp. One tool per (FM type, model) cell plus per-type ensemble + listing. Tabular is HTTP-only.
• Auth — optional bearer token (PREDICTALOT_AUTH_TOKENS on the server; refuses to start with no tokens unless PREDICTALOT_ALLOW_NO_AUTH=1).

For installation, configuration, and container setup, see references/setup.md.

Security & safety

• Network-exposed — predictalot is a plain HTTP + MCP service; anyone who can reach the port can call it. Set PREDICTALOT_AUTH_TOKENS to a strong generated secret ($(openssl rand -hex 32)) and bind to loopback (-p 127.0.0.1:8080:8080) by default. Only expose beyond loopback behind a reverse proxy / VPN, and never with the default/example token.
• External transmission — every forecast/train call sends your time series, engineered feature values, and/or modelIds to whatever PREDICTALOT_URL points at — that data leaves your host. Point it only at a service you run or explicitly trust; prefer HTTPS.
• Consumer-only — this skill talks to an instance you already run and trusts. It never provisions, starts, or hardens the server; that's the operator's job (see setup.md).
• Destructive delete requires confirmation — DELETE /v1/tabular/models/{modelId} permanently removes a trained model and cannot be undone. Only call it against a modelId you obtained from a prior GET /v1/tabular/models (or a train response) in this session, and get explicit user confirmation before issuing the delete.

When To Use

• Forecast a numeric time series N steps ahead and get calibrated quantile bands (0.1/0.5/0.9, etc.) — zero-shot, no training.
• Get raw Monte-Carlo sample paths (via the samples type) to compute custom risk metrics / joint distributions across horizon steps.
• Condition a forecast on covariates: known-history (covariates/past), forward-known drivers like a planned promotion or price schedule (covariates/future), or both at once (covariates).
• Forecast several correlated channels jointly (multivariate).
• Combine multiple forecasters into a weighted ensemble and inspect each member's individual forecast + applied weight.
• Train a supervised model on engineered features and predict next-bar direction (P(up)), a point value, or quantiles on the latest snapshot.
• Combine trained tabular models via ensemble or a calibrated / stacking / diversified meta-learner.

When NOT To Use

• Real-time / streaming forecasts — every endpoint is request/response only.
• Automatic feature engineering on the tabular side — you supply the features; predictalot does not derive indicators or lags for you.
• timesfm-2.5 for accuracy — it is the weakest of the five on every benchmarked dataset. Skip it or set weights={"timesfm-2.5": 0} in ensembles.
• Commercial use of moirai-2 — it ships under CC-BY-NC-4.0 (non-commercial). The other four FMs are Apache 2.0.
• Covariate/multivariate/samples types on models that don't support them — membership is fixed per type (see below). A non-member model → 400.
• Provisioning or hardening the server from here — this skill is a consumer. It talks to an instance the user already runs and trusts; it never launches, escalates, or reconfigures the container.

Setup

The container should already be running. Point at it:

bash

export PREDICTALOT_URL=http://localhost:8080

If the server has PREDICTALOT_AUTH_TOKENS set, export a token too:

bash

export PREDICTALOT_AUTH_TOKEN=<your-token>
# every /v1/* and /mcp request below needs: -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN"

Verify: curl $PREDICTALOT_URL/healthz returns {"ok": true}. (/healthz is unauthenticated.)

For install / configuration / env vars / CPU vs CUDA images, see references/setup.md.

Models & Types

Foundation models, and which forecast types each supports (a model outside a type's member set → 400):

Model	Univariate	Multivariate	Cov: past	Cov: future	Cov: both	Samples	License	Recommended for
chronos-2	✓	✓	✓	✓	✓	—	Apache 2.0	Default all-rounder; fastest on CPU; only model with future/both covariates.
timesfm-2.5	✓	—	—	—	—	—	Apache 2.0	Weakest on benchmarks — skip or zero-weight it. Univariate-only; compile-time horizon cap.
moirai-2	✓	✓	✓	—	—	—	CC-BY-NC-4.0	Clean cyclic/seasonal series; correlated channels. Non-commercial license.
toto-1	✓	✓	—	—	—	✓	Apache 2.0	Noisy / observability / financial series; exposes raw sample paths.
sundial-base-128m	✓	—	—	—	—	✓	Apache 2.0	Drifting / trending series; generative sample paths. Runs in a sidecar venv.

Type → URL prefix:

Type	URL prefix	Members
univariate	/v1/timeseries/univariate	chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m
multivariate	/v1/timeseries/multivariate	chronos-2, moirai-2, toto-1
covariates (past)	/v1/timeseries/covariates/past	chronos-2, moirai-2
covariates (future)	/v1/timeseries/covariates/future	chronos-2
covariates (past+future)	/v1/timeseries/covariates	chronos-2
samples	/v1/timeseries/samples	toto-1, sundial-base-128m

Discover live per-type membership + runtime state with GET /v1/timeseries/<type>/models. Discover tabular backends with GET /v1/tabular/backends.

Tabular backends (all support direction / value / quantile):

Slug	Display name	Category
lightgbm	LightGBM	boosting
xgboost	XGBoost	boosting
hist-gbt	HistGradientBoosting (sklearn)	boosting
random-forest	Random Forest	bagging
logistic	Logistic / Ridge / QuantileRegressor (linear baselines)	linear
mlp	Multi-Layer Perceptron (sklearn)	neural
svm-rbf	SVM with RBF kernel	kernel
knn	k-Nearest Neighbors	distance
naive-bayes	Gaussian Naive Bayes / BayesianRidge	independence

Quick Start

Every forecast/train call below sends your time series, engineered features, and/or modelIds over the network to $PREDICTALOT_URL. Only point this at a trusted, self-hosted instance you control, prefer HTTPS, treat proprietary datasets as sensitive, and never echo PREDICTALOT_AUTH_TOKEN in output.

bash

# Health (unauthenticated).
curl -s $PREDICTALOT_URL/healthz | jq

# List univariate models + their runtime state.
curl -s $PREDICTALOT_URL/v1/timeseries/univariate/models \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" | jq

# Zero-shot univariate forecast: 5 steps ahead of one series, chronos-2.
curl -s $PREDICTALOT_URL/v1/timeseries/univariate/forecast \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "chronos-2",
        "context": [[10,11,12,13,14,15,16,17,18,19,20]],
        "config": { "horizon": 5, "quantileLevels": [0.1, 0.5, 0.9] }
      }' | jq

Wire format is camelCase (quantileLevels, contextLength, pastCovariates, futureCovariates, numSamples, memberOverrides, modelId). All /v1/* routes require the bearer header when the server has tokens configured; drop the header for an open-auth deployment.

Shapes recur across the timeseries API: context is [series][time] (a batch of independent series), horizon > 0, quantileLevels is a subset of {0.1, 0.2, …, 0.9} (default [0.1, 0.5, 0.9]), contextLength caps history fed to the model (omit → per-model default), unload: true tears the model down after the response. median and quantiles["0.5"] are the same for most models but can differ for chronos-2 (its median is the distribution mean).

---

API — POST /v1/timeseries/univariate/forecast

Single-model quantile forecast over a batch of independent series. context (your time series data) transmits off-box to $PREDICTALOT_URL — same data-transfer note as Quick Start applies to every timeseries endpoint below.

Request Fields

Field	Required	Default	Notes
model	yes	—	One of the univariate members. Not a member → 400; unknown slug → 404.
context	yes	—	[series][time] — one inner list of floats per series.
config.horizon	yes	—	Steps ahead. Must be > 0 (else 422).
config.quantileLevels	no	[0.1, 0.5, 0.9]	Subset of {0.1..0.9} step 0.1. Out-of-range → 400.
config.contextLength	no	per-model	Max history points fed to the model. > 0.
config.extra	no	—	Per-backend escape-hatch dict, camelCased. Unknown keys ignored. E.g. chronos-2: batchSize, crossLearning, limitPredictionLength; toto-1: numSamples, samplesPerBatch, useKvCache; timesfm-2.5: normalizeInputs, fixQuantileCrossing, …
unload	no	false	Unload the model after responding.

Response

json

{
  "model": "chronos-2",
  "horizon": 5,
  "quantileLevels": [0.1, 0.5, 0.9],
  "median": [[20.9, 21.8, 22.7, 23.6, 24.5]],
  "quantiles": {
    "0.1": [[20.1, 20.8, 21.5, 22.1, 22.7]],
    "0.5": [[20.9, 21.8, 22.7, 23.6, 24.5]],
    "0.9": [[21.7, 22.9, 24.0, 25.1, 26.3]]
  }
}

median is [series][time]; quantiles maps each level string → [series][time].

Error Contract

Status	Shape	When
200	forecast JSON	success
400	{"detail": "..."}	empty context/series, model not a member of the type, quantile level outside {0.1..0.9}, horizon over a model's compile-time cap (timesfm-2.5 / moirai-2)
401	{"detail": "..."}	tokens configured, missing/wrong bearer
404	{"detail": "..."}	unknown model slug
413	{"detail": "..."}	body over PREDICTALOT_MAX_BODY_SIZE
422	{"detail": [...]}	Pydantic validation (missing/typed fields, horizon not > 0)
503	{"detail": "..."}	snapshot download failed, inference threw, or sundial sidecar unreachable

---

API — POST /v1/timeseries/univariate/forecast/ensemble

Weighted mean across every univariate member. No model field — membership is the whole type.

Request Fields

Field	Required	Default	Notes
context	yes	—	[series][time].
config	yes	—	Same ForecastConfig as single forecast (horizon, quantileLevels, contextLength, extra).
weights	no	uniform	{slug: float ≥ 0}. Missing slugs default to 1.0; weight 0 disables a member; unknown slug → 400.
memberOverrides	no	—	{slug: partial-config} — per-member overrides of the global config (e.g. give one member a different contextLength or extra).
unload	no	false	Unload members after responding.

Response

Aggregated median + quantiles (same shapes as single forecast) with model: "ensemble", plus:

json

{
  "model": "ensemble",
  "horizon": 5,
  "quantileLevels": [0.1, 0.5, 0.9],
  "median": [[...]],
  "quantiles": { "0.1": [[...]], "0.5": [[...]], "0.9": [[...]] },
  "ensembleMembers": ["chronos-2", "moirai-2", "toto-1"],
  "weights": { "chronos-2": 0.5, "moirai-2": 0.25, "toto-1": 0.25 },
  "individual": {
    "chronos-2": { "model": "chronos-2", "horizon": 5, "quantileLevels": [...],
                   "median": [[...]], "quantiles": {...}, "weight": 0.5 }
  }
}

individual[slug] is that member's full single-forecast response + its applied weight.

Error Contract

Same as single univariate, minus 404 (no model field) — an unknown weights slug is a 400, not 404.

---

API — POST /v1/timeseries/covariates/past/forecast

Forecast each target series conditioned on covariates known only up to t. Members: chronos-2, moirai-2.

Request Fields

Field	Required	Default	Notes
model	yes	—	A past-covariate member.
context	yes	—	[series][time] — the univariate targets.
pastCovariates	yes	—	list[dict[name, float[]]] — one mapping per series; every value array is the same length as that series' context. All series share the same covariate names.
config	yes	—	ForecastConfig (horizon, quantileLevels, contextLength, extra).
unload	no	false

Response

Identical shape to univariate single forecast (model, horizon, quantileLevels, median, quantiles).

Error Contract

Same table as univariate, plus 400 when a pastCovariates value length doesn't match the matching context series.

---

API — POST /v1/timeseries/covariates/past/forecast/ensemble

Weighted ensemble over the past-covariate members. Fields = past forecast fields plus weights + memberOverrides (as in the univariate ensemble). Response = univariate ensemble shape.

---

API — POST /v1/timeseries/covariates/future/forecast

Forecast conditioned on covariates known only over the future window (length == horizon) — e.g. a planned promotion, a published price schedule, a weather forecast. Member: chronos-2 only.

Request Fields

Field	Required	Default	Notes
model	yes	—	chronos-2.
context	yes	—	[series][time] targets.
futureCovariates	yes	—	list[dict[name, float[]]] — one mapping per series; each value array of length horizon. All series share names.
config	yes	—	ForecastConfig.
unload	no	false

Response

Univariate single-forecast shape.

Error Contract

Univariate table + 400 when a futureCovariates value length ≠ config.horizon.

---

API — POST /v1/timeseries/covariates/future/forecast/ensemble

Ensemble over future-covariate members (currently just chronos-2, so only useful once more back it). Fields = future forecast + weights + memberOverrides. Response = univariate ensemble shape.

---

API — POST /v1/timeseries/covariates/forecast (past + future)

Combined past and future covariates in one call. Member: chronos-2 only.

Request Fields

Field	Required	Default	Notes
model	yes	—	chronos-2.
context	yes	—	[series][time] targets.
pastCovariates	yes	—	Per series, same length as context.
futureCovariates	yes	—	Per series, length horizon. Every future-covariate name MUST also appear in pastCovariates for that series (chronos-2 constraint) — else 400.
config	yes	—	ForecastConfig.
unload	no	false

Response

Univariate single-forecast shape.

Error Contract

Univariate table + 400 for length mismatches or a future-covariate name missing from pastCovariates.

---

API — POST /v1/timeseries/covariates/forecast/ensemble

Ensemble over past+future members. Fields = past+future forecast + weights + memberOverrides. Response = univariate ensemble shape.

---

API — GET /v1/timeseries/<type>/models

Per-type model listing + runtime state. <type> ∈ univariate, multivariate, covariates/past, covariates/future, covariates, samples.

Response

json

{
  "type": "univariate",
  "models": [
    { "slug": "chronos-2", "loaded": true, "lastUsedSecsAgo": 12.4, "idleTimeoutSecs": 1800.0 },
    { "slug": "timesfm-2.5", "loaded": false, "lastUsedSecsAgo": null, "idleTimeoutSecs": 1800.0 }
  ]
}

lastUsedSecsAgo is null when the model has never loaded. idleTimeoutSecs reflects PREDICTALOT_MODEL_IDLE_TIMEOUT (or a per-slug override); 0 means never auto-unloaded.

Error Contract

200 on success; 401 when tokens are configured and the bearer is missing/wrong.

---

API — GET /v1/tabular/backends

List the supervised tabular backends and the modes each supports. Use this to discover the live set before a train call.

Response

json

{
  "backends": [
    { "slug": "lightgbm", "displayName": "LightGBM", "category": "boosting",
      "supportedModes": ["direction", "quantile", "value"] },
    { "slug": "xgboost", "displayName": "XGBoost", "category": "boosting",
      "supportedModes": ["direction", "quantile", "value"] }
  ]
}

Error Contract

200 on success; 401 when tokens are configured and the bearer is missing/wrong.

---

Tabular API (train → forecast)

The tabular side trains on YOUR engineered features and persists a model server-side by modelId. target / features are generic float lists — no OHLC / indicator assumptions. features is per series: featureName → time-aligned float list. Same data-transfer note as Quick Start applies: target/features/modelId all transmit to $PREDICTALOT_URL.

POST /v1/tabular/train

Field	Required	Default	Notes
modelId	yes	—	Caller-chosen id, used for storage + later forecast lookup.
backend	yes	—	A slug from GET /v1/tabular/backends. Unknown → 404.
target	yes	—	[series][time] — the scalar series to predict. Empty → 400.
features	yes	—	list[dict[name, float[]]] — same length + names per series as target. Count must match target (else 400); differing key sets across series → 400.
config.mode	yes	—	direction (sign of target[t+h] - target[t]) / value (regress target[t+h]) / quantile. Mode not supported by the backend → 400.
config.horizon	yes	—	Bars ahead (t+h). > 0.
config.quantileLevels	when quantile	—	Required for mode="quantile"; subset of {0.1..0.9}.
config.* (tier-2)	no	—	nEstimators, maxDepth, learningRate, numLeaves, minSamples, randomState, categoricalFeatures, monotonicConstraints, classWeight, sampleWeight, earlyStoppingRounds, validationFraction. Backends ignore knobs they don't use.
config.extra	no	—	Per-backend hyperparams (e.g. svm-rbf reads C/gamma; mlp reads hiddenLayerSizes/activation; knn reads nNeighbors/weights/metric).
overwrite	no	false	Reuse an existing modelId → 409 unless true.

Response (TrainResponse): modelId, backend, mode, horizon, nTrainingRows, nFeatures, featureNames, featureImportance ({name: float}), trainSecs.

POST /v1/tabular/forecast

Runs a stored model on the latest feature snapshot — the LAST row per series is the anchor; earlier rows are ignored.

Field	Required	Notes
modelId	yes	Missing → 404. If its backend is no longer registered → 410.
features	yes	list[dict[name, float[]]]; names must match training. Missing a trained name → 400.

Response (ForecastResponse) is mode-dependent:

• direction: probUp: float[] (per series) + confidence: float[] (|probUp-0.5|*2).
• value: predicted: float[].
• quantile: median: float[series][1] + quantiles: {level: float[series][1]}.

POST /v1/tabular/forecast/ensemble

Weighted combination of several stored models on the same features; all members must share mode, horizon, and featureNames (mismatch → 400).

Field	Required	Notes
modelIds	yes	Stored ids to combine (≥ 1). Any missing → 404.
weights	no	{modelId: float ≥ 0}. None → uniform; 0 removes a member; unknown id or negative → 400.
features	yes	As in forecast; last row per series is the anchor.

Response (EnsembleForecastResponse): mode, horizon, ensembleMembers, normalized weights, individual ({modelId: member response}), plus the combined mode-specific fields (probUp+confidence / predicted / median+quantiles).

GET /v1/tabular/models and DELETE /v1/tabular/models/{modelId} (destructive)

GET lists stored models: {"models": [{ modelId, backend, mode, horizon, nFeatures, featureNames, nTrainingRows, trainedAtUnix }]}. DELETE removes one ({"modelId": "...", "removed": true}; 404 if absent) — irreversible. Only delete a modelId returned by a prior GET /v1/tabular/models call (or a train response), and get explicit user confirmation first.

Meta-learners

Composite endpoints that hold out / cross-validate internally so you don't have to orchestrate it client-side. Each persists one blob under a meta:<kind> backend tag and forecasts via POST /v1/tabular/forecast/<kind> with {modelId, features}.

• POST /v1/tabular/train/calibrated — base learner + post-hoc probability calibrator. direction only. Fields: modelId, baseBackend, target, features, config (mode must be direction), calibrationMethod (sigmoid|isotonic, default sigmoid), calibrationFraction (default 0.2, in (0,1)), overwrite. Too-small split → 400.
• POST /v1/tabular/train/stacking — K base learners + a meta-learner on K-fold OOF predictions. direction only (v1). Fields: modelId, members (≥ 2 {backend, config}, all direction, horizon == top-level), metaBackend (default logistic), target, features, horizon, nFolds (2–10, default 5), overwrite. Needs ≥ nFolds*5 rows. Response includes oofScore (AUC).
• POST /v1/tabular/train/diversified — trains K candidates, selects a low-correlation subset by OOF Pearson correlation, combines equal-weight. Supports all 3 modes. Fields: modelId, candidates (≥ 2, mode+horizon must match top-level), target, features, horizon, mode, quantileLevels (required if quantile), nFolds (2–10, default 3), maxPairwiseCorr (0–1, default 0.85), minMembers, maxMembers, overwrite. Response includes candidateCorr.

Meta-train responses (MetaTrainResponse): modelId, kind, mode, horizon, membersUsed, nTrainingRows, nFeatures, featureNames, trainSecs, plus oofScore (stacking) / candidateCorr (diversified). Meta-forecast responses (MetaForecastResponse): modelId, kind, mode, horizon, members (per-member breakdown), selectedMembers (diversified), and the mode-specific combined fields.

Tabular Error Contract

Status	When
200	success
400	empty target; target/features count mismatch; differing feature key sets; mode unsupported by backend; quantile without quantileLevels; forecast features missing a trained name; ensemble member mode/horizon/features mismatch; unknown/negative ensemble weight; meta constraint violated (calibrated non-direction, stacking non-direction, diversified quantile w/o levels, split too small)
401	tokens configured, missing/wrong bearer
404	unknown tabular backend on train; modelId not found on forecast/meta
409	train with overwrite: false against an existing modelId
410	forecast against a modelId whose backend is no longer registered
413	body over PREDICTALOT_MAX_BODY_SIZE
422	Pydantic validation
503	training/inference threw

---

MCP Endpoint (/mcp)

predictalot mounts a Model Context Protocol server over Streamable HTTP at /mcp, in the same process, behind the same bearer auth. The tool surface mirrors the foundation-model routes only — tabular is HTTP-only.

For each forecast type there are three classes of tool:

• forecast_<type>_<model> — single-model forecast for one (type, model) cell. Types are underscore-normalized: univariate, multivariate, covariates_past, covariates_future, covariates_both, samples; model slugs are underscore-normalized too (chronos-2 → chronos_2). E.g. forecast_univariate_chronos_2, forecast_covariates_future_chronos_2.
• forecast_<type>_ensemble — weighted ensemble over every model supporting that type.
• list_<type>_models — which models implement the type + runtime state ({type, models: [{slug, loaded, lastUsedSecsAgo, idleTimeoutSecs}]}).

Tool args mirror the HTTP bodies but flattened (no nested config): context, horizon, quantile_levels?, context_length?, unload?; covariate tools add past_covariates / future_covariates; ensemble tools add weights?; samples tools use num_samples? instead of quantile levels and return samples [series][sample][time] + median.

Wire it into Claude Code (auth optional — the token may also be passed as ?apiToken=<token>):

bash

claude mcp add --transport http predictalot $PREDICTALOT_URL/mcp \
  --header "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN"

Raw JSON-RPC

The transport requires Accept: application/json, text/event-stream.

bash

# tools/list — enumerate every (type, model) tool + ensembles + listings.
curl -s $PREDICTALOT_URL/mcp/ \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}'

# tools/call — univariate chronos-2 forecast.
curl -s $PREDICTALOT_URL/mcp/ \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{
    "jsonrpc": "2.0", "id": 2, "method": "tools/call",
    "params": {
      "name": "forecast_univariate_chronos_2",
      "arguments": {
        "context": [[10,11,12,13,14,15,16,17,18,19,20]],
        "horizon": 5,
        "quantile_levels": [0.1, 0.5, 0.9]
      }
    }
  }'

Each tool returns a JSON-encoded string. User-input errors come back as {"error": "...", "context": "<type>/<model>"}; internal errors are redacted to {"error": "internal error; see server logs", "context": "..."}.

Bearer-Token Auth

If PREDICTALOT_AUTH_TOKENS (comma-separated list) is set on the server, every /v1/* and /mcp request needs Authorization: Bearer <one-of-the-tokens>; /healthz is always open. Missing/wrong → 401. Tokens are compared constant-time (hmac.compare_digest).

bash

export PREDICTALOT_AUTH_TOKEN=<your-token>
curl -s $PREDICTALOT_URL/v1/timeseries/univariate/models \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" | jq

The server refuses to start with an empty token list unless it was launched with PREDICTALOT_ALLOW_NO_AUTH=1 — in that open-auth mode there is no 401 and no header is needed. For untrusted networks, combine the token with a reverse proxy doing TLS + rate limiting.

Typical Workflows

Pick a model → forecast → read the intervals

bash

# 1. See which univariate models are available + resident.
curl -s $PREDICTALOT_URL/v1/timeseries/univariate/models \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" | jq -r '.models[].slug'

# 2. Forecast with chronos-2 (default all-rounder), 12 steps, three bands.
curl -s $PREDICTALOT_URL/v1/timeseries/univariate/forecast \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model":"chronos-2","context":[[100,102,101,105,110,108,112,115,120,118,125,130]],
       "config":{"horizon":12,"quantileLevels":[0.1,0.5,0.9]}}' | jq

# 3. Read the interval: quantiles["0.5"] is the central path,
#    ["0.1"]/["0.9"] the 80% band — all shaped [series][time].

Ensemble, zero-weighting the weak model

bash

curl -s $PREDICTALOT_URL/v1/timeseries/univariate/forecast/ensemble \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"context":[[100,102,101,105,110,108,112,115,120,118,125,130]],
       "config":{"horizon":12},
       "weights":{"timesfm-2.5":0,"chronos-2":2,"toto-1":1}}' | jq \
  '{members: .ensembleMembers, weights, median}'

Future-covariate forecast (chronos-2)

bash

# Target has 8 steps of history; horizon 4 → futureCovariates arrays length 4.
curl -s $PREDICTALOT_URL/v1/timeseries/covariates/future/forecast \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model":"chronos-2",
       "context":[[20,22,21,25,24,27,29,31]],
       "futureCovariates":[{"promo":[0,1,1,0]}],
       "config":{"horizon":4}}' | jq

Raw sample paths for custom risk metrics

bash

curl -s $PREDICTALOT_URL/v1/timeseries/samples/forecast \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model":"toto-1",
       "context":[[100,102,101,105,110,108,112,115,120,118,125,130]],
       "config":{"horizon":10,"numSamples":256}}' | jq '{numSamples, shape: (.samples[0]|length)}'
# samples is [series][sample][time]; compute your own VaR / quantiles off the draws.

Train a tabular direction model, then forecast the latest snapshot

bash

# 1. Train on engineered features (you supply them).
curl -s $PREDICTALOT_URL/v1/tabular/train \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"modelId":"trend-3","backend":"lightgbm",
       "target":[[100,101,99,102,105,103,107,110,108,112]],
       "features":[{"rsi":[55,58,52,60,63,59,65,70,66,72],
                    "mom":[0.2,0.3,-0.1,0.4,0.5,0.2,0.6,0.7,0.4,0.8]}],
       "config":{"mode":"direction","horizon":3,"nEstimators":400}}' | jq

# 2. Forecast — LAST feature row is the anchor.
curl -s $PREDICTALOT_URL/v1/tabular/forecast \
  -H "Authorization: Bearer $PREDICTALOT_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"modelId":"trend-3","features":[{"rsi":[72],"mom":[0.8]}]}' | jq
# → {"modelId":"trend-3","backend":"lightgbm","mode":"direction","horizon":3,
#    "probUp":[0.63],"confidence":[0.26]}

For a driver that runs a univariate forecast and pretty-prints the interval, see scripts/predictalot.sh:

bash

PREDICTALOT_URL=http://localhost:8080 \
PREDICTALOT_AUTH_TOKEN=<token> \
  bash scripts/predictalot.sh chronos-2 5 10 11 12 13 14 15 16 17 18 19 20

Tips

1. Default to chronos-2 — fastest on CPU, widest type coverage (only model with future/both covariates), solid general-purpose accuracy.
2. Skip or zero-weight timesfm-2.5 — weakest on every benchmark. In ensembles pass weights={"timesfm-2.5":0}.
3. moirai-2 is CC-BY-NC-4.0 — non-commercial. Fine for research/eval, not for a commercial product.
4. Use samples for risk work — toto-1 / sundial-base-128m return raw [series][sample][time] paths; compute your own VaR / joint metrics instead of trusting fixed quantile cuts.
5. median ≠ quantiles["0.5"] for chronos-2 — its median is the distribution mean; the others agree.
6. Covariate length rules — past covariates match the context length; future covariates match horizon; in the past+future type every future-cov name must also be a past-cov name.
7. Tabular features are yours — the API does no feature engineering. The LAST row per series is the forecast anchor.
8. overwrite: false is the default on every train endpoint — re-training a known modelId is a 409 until you pass overwrite: true.
9. Discover, don't assume — GET /v1/timeseries/<type>/models and GET /v1/tabular/backends are the live source of truth for membership and modes.
10. First call is a cold load — a model not yet resident pays a load (and download, if the snapshot isn't cached) on first request; subsequent calls are fast until the idle sweeper unloads it.