Flat trends

If your data moves around a steady level, with no long-term growth or decline, use growth: "flat". The trend becomes a single constant, and all the movement comes from seasonal patterns, events and regressors.

import { Effect } from "effect";
import { fit, predict, prophetFittingBackendLayer } from "effect-prophet";

const date = (day: number) => new Date(Date.UTC(2025, 0, 1 + day)).toISOString();

// Daily support tickets: a steady level with a weekly pattern, no long-term growth.
const history = Array.from({ length: 84 }, (_, day) => ({
  timestamp: date(day),
  value: 40 + 12 * Math.sin((2 * Math.PI * day) / 7) + 2 * Math.sin(day * 12.9898),
}));

const forecast = await Effect.runPromise(
  Effect.gen(function* () {
    const model = yield* fit(history, { growth: "flat" });

    return yield* predict(model, [date(84), date(85), date(86)]);
  }).pipe(Effect.provide(prophetFittingBackendLayer)),
);

console.log(forecast.map((row) => `${row.value.toFixed(1)} (trend ${row.trend.toFixed(1)})`));

[ '40.1 (trend 40.1)', '49.5 (trend 40.1)', '51.8 (trend 40.1)' ]

Verified against Python Prophet

Flat trends match Python Prophet 1.4.0. The flat growth benchmarks show every case side by side.

When to use it

• The data has no real trend, and you don't want the model to invent one from a few unusual weeks.
• You are forecasting far ahead and would rather assume "same as now" than extend a slope.

Changepoint settings

map.changepoints and map.changepointPriorScale work with growth: "flat", as they do in Python. They never bend a flat trend. Prophet still fits a slope and one adjustment per changepoint alongside the level, held in place only by their priors, so these settings decide how many of those values there are and how tightly they are held. In exact arithmetic that leaves the fitted level and effects unchanged. In practice it changes the path the fit takes, so forecasts can differ in the last digits, and fewer changepoints make each fitting step cheaper. Pass the values you would pass Python to get the same fit.