{"uid":"cap_ivbODzllVWzYK95B-1ww5","slug":"holt-winters-triple-exponential-smoothing-forecast-b41ebd6f","name":"Holt-Winters Triple Exponential Smoothing Forecast","description":"Holt-Winters triple exponential smoothing - level + trend + seasonal component. Use for series with a repeating cycle (weekly retail traffic, monthly utility usage, quarterly revenue). Additive seasonality (constant amplitude) or multiplicative (amplitude grows with level). `period` is optional - if omitted, the kit auto-detects via autocorrelation on first differences and surfaces what it picked (with the ACF strength) so you can audit. Needs at least two full seasonal cycles to fit reliably.","url":"https://agent402.tools/api/forecast-holt-winters","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"beta":{"type":"number","description":"Trend smoothing, 0 < beta < 1 (default 0.1)"},"alpha":{"type":"number","description":"Level smoothing, 0 < alpha < 1 (default 0.5)"},"gamma":{"type":"number","description":"Seasonal smoothing, 0 < gamma < 1 (default 0.1)"},"period":{"type":"number","description":"Optional seasonal period (e.g. 7 for daily/weekly, 12 for monthly/yearly). Auto-detected via ACF if omitted."},"values":{"type":"array","description":"Numeric series in chronological order (max 10000)"},"horizon":{"type":"number","description":"Number of future periods to forecast (1 to 1000)"},"seasonality":{"type":"string","description":"\"additive\" (default) or \"multiplicative\""}}},"responseSchema":{"type":"json","example":{"n":12,"beta":0.1,"alpha":0.5,"gamma":0.1,"method":"holt-winters","period":4,"horizon":4,"forecast":[{"step":1,"point":12.5324,"lower95":11.6958,"upper95":13.369},{"step":2,"point":16.7253,"lower95":15.5421,"upper95":17.9084},{"step":3,"point":20.9462,"lower95":19.4972,"upper95":22.3952},{"step":4,"point":25.1831,"lower95":23.5098,"upper95":26.8563}],"seasonality":"additive","periodSource":"provided"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ZCBKXrcYaElgFGicjzzbk","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Forecasts future values of a time series with level, trend, and seasonal components using Holt-Winters triple exponential smoothing (additive or multiplicative seasonality).","exampleAgentPrompt":"I have 104 weeks of weekly retail traffic data — can you run a Holt-Winters forecast on it with a period of 7, additive seasonality, alpha 0.4, beta 0.1, gamma 0.2, and predict the next 12 periods?","exampleUseCases":null,"resultDescription":"Returns forecasted numeric values for the requested horizon, along with decomposed level, trend, and seasonal components derived from the Holt-Winters algorithm applied to the input series.","failureModes":["Series too short relative to the seasonal period — insufficient data to estimate seasonal indices","Invalid alpha/beta/gamma values outside (0,1) range — returns validation error","Period auto-detection fails if the series lacks a clear repeating cycle — may return error or fallback estimate","Horizon exceeds 1000 — returns out-of-range error","Values array exceeds 10000 elements — returns payload too large error","Multiplicative seasonality with zero or negative values causes division errors"],"whenToPreferThis":"Choose this endpoint when your time series exhibits both a trend and a repeating seasonal cycle (e.g. weekly, monthly, quarterly patterns). It is preferable over simple or double exponential smoothing when seasonality is present, and preferable over ARIMA or ML-based approaches when you need a fast, interpretable, parameter-controlled forecast without training data requirements.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T19:14:06.935Z","isFirstParty":false}