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Empty = by frequency: 12 months, 30 days, 13 weeks, 8 quarters, 5 years, 48 hours." }, "frequency": { "title": "Frequency", "enum": [ "auto", "minute", "5min", "10min", "15min", "30min", "hour", "day", "businessDay", "week", "month", "quarter", "year" ], "type": "string", "description": "Step of the series. Choosing a coarser step than your data aggregates it (e.g. daily orders → weekly totals, see \"Combine values\"). Without dates, set it to get dates on the forecast (JSON series with \"start\").", "default": "auto" }, "quantiles": { "title": "Prediction interval quantiles", "type": "array", "description": "Quantiles to return besides the median, between 0.01 and 0.99 (0.1 and 0.9 = an 80% interval; 0.025 and 0.975 = 95%). Output keys are q10, q90, q2.5…", "default": [ 0.1, 0.9 ] }, "model": { "title": "Model", "enum": [ "timestral-123m", "timestral-28m" ], "type": "string", "description": "Mistral AI's Timestral zero-shot forecasting models, run on CPU inside this Actor. Same price for both.", "default": "timestral-123m" }, "evaluate": { "title": "Backtest against a simple baseline", "type": "boolean", "description": "Also forecast the last part of the history as if it were unknown and compare the error with a seasonal-naive forecast (repeat last season). Adds a \"backtest\" object to each row; no extra charge, about twice the run time.", "default": false }, "series": { "title": "Or JSON series (API)", "type": "array", "description": "For API and AI-agent use: [{\"id\": \"sku-1\", \"values\": [12, 15, 14], \"timestamps\": [\"2026-01-01\", …]}] or with \"start\": \"2026-01-01\" and \"freq\": \"D\" | \"W\" | \"M\" | \"Q\" | \"Y\" | \"H\". Also [[date, value], …] pairs and plain arrays of numbers." }, "dateColumn": { "title": "Date column", "type": "string", "description": "Header name or column number (1 = first) of the dates. 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