POST /api/v1/forecasts

Starts an async forecast job. Returns 202 Accepted with job_id (UUID) and poll_url.

Before charging: rate limits, concurrent-job cap, and the user's available balance for the hold may return 429 or 402 — see Errors & limits. Balances on GET /api/v1/me (available_eur_cents, balance_eur_cents) are in EUR cents.

Request body

Required top-level fields: pipeline_version, frequency, recency_factor, timeseries_metadata, timeseries. At least one of soft_horizon or hard_horizon must be present. Optional: backtest, strictly_positive, filters.

pipeline_version

Must be exactly "v1" (case-sensitive). No "latest" alias.

soft_horizon

Integer 1–12 inclusive. Optional, but at least one of soft_horizon or hard_horizon must be present.

The ideal forecast horizon in months. The pipeline tries this length first, then steps down one month at a time toward hard_horizon (when set) while seeking a quality forecast. When only soft_horizon is given and no quality run succeeds, the pipeline falls back to a driverless forecast at soft_horizon.

hard_horizon

Integer 1–12 inclusive. Optional, but at least one of soft_horizon or hard_horizon must be present.

The minimum acceptable horizon in months for the quality step-down ladder. When hard_horizon is reached and the pipeline still cannot produce a quality run, it emits a driverless forecast at hard_horizon.

When both fields are provided, hard_horizon must be less than or equal to soft_horizon — submitting hard_horizon > soft_horizon returns 422.

frequency

Only "monthly" is supported in v1. "daily" / "weekly" return a clear not supported error; other values return unknown frequency.

backtest

Boolean, optional, defaults to false.

When true, the pipeline runs a rolling-window backtest evaluation alongside the forecast. Two additional artifacts become available on the settled job:

• backtest_trajectories.json — per-fold actual vs forecast series (last 12 months retained).
• backtest_metrics.json — aggregated MAPE and RMSE metrics over rolling 6m / 12m / 24m / 60m windows.

strictly_positive

Boolean, optional, defaults to false.

When true, the request must satisfy two halves of the same contract:

• Input rule (validator): every value in timeseries must be >= 0 (zero is allowed). A single negative observation rejects the request with 422, fail-fast — only the first offending key is reported, with field timeseries["YYYY-MM-DD"] and a message naming the value and the flag.
• Output behavior (pipeline): the forecasting pipeline clamps the produced forecast at zero so no output point can be negative.

When false (or omitted) neither the input rule nor the output clamp is applied; negative observations are accepted and negative forecast points are returned unchanged.

recency_factor

Number 0.0–1.0 inclusive.

Controls how strongly recent news is used to augment the dataset search with related context. A value closer to 0.0 uses a broader historical news window, up to January 2020. A value closer to 1.0 places stronger emphasis on recent news, up to the latest week.

This has a significant impact on the drivers selected by the system and, consequently, on forecast quality.

timeseries_metadata

Field	Rules
title	Required string, byte length ≥ 20 and ≤ 511 (not trimmed).
description	Optional; if present, ≤ 2048 bytes.
keywords	Optional array; if present, ≤ 20 items; each non-empty, each ≤ 255 bytes.

Keywords dramatically affect forecast quality

keywords has a significant impact on the drivers selected by the system and, consequently, on the quality of the forecast. Include both direct dataset terms and broader domain knowledge — the more relevant context you provide, the better the driver selection.

Example — Aluminium Price:aluminium price, aluminium demand, bauxite, alumina, smelting costs, electricity prices, energy-intensive production, Chinese industrial demand, construction activity, automotive demand, inventories, production cuts, sanctions, trade flows, freight costs, macroeconomic indicators

Example — Textile Demand:textile demand, apparel demand, clothing sales, retail sales, consumer confidence, disposable income, inflation, fashion retail, e-commerce sales, clothing inventories, import/export flows, manufacturing activity, cotton prices, polyester prices, freight costs, energy costs

filters (optional, top-level)

If omitted or null, the whole object is ignored. When present:

• regions[]: region ids (the id field from GET /api/v1/regions) — see Regions & categories. categories[]: category ids (the id field from GET /api/v1/categories). The API does not cross-check either on submit.
• limit: optional integer 0–1000. Defaults to the maximum when omitted. Has no direct effect on forecast billing; it is forwarded to the pipeline to control how many drivers are considered.

timeseries

Object mapping YYYY-MM-DD keys → finite numbers.

Calendar

Dates use the Gregorian calendar. For monthly frequency, each key must be the first calendar day of the month (YYYY-MM-01); the API error text refers to this as month alignment. Recency checks compare against UTC dates, so accounts in non-UTC timezones see consistent weighting.

1. Must be non-empty.
2. Values must be finite (no NaN/Inf).
3. Keys must be valid calendar dates; for monthly frequency, keys must be aligned (first day of month — see API error text).
4. Series must have no gaps in the monthly grid (first issue wins).
5. Minimum length depends on horizonMax (monthly points), where horizonMax = max(soft_horizon, hard_horizon); when only one is given, horizonMax equals that value:

horizonMax	Minimum monthly points
1–3	40
4–6	60
7–12	120

6. Recency: the latest observation must be within the past 12 months (UTC-relative comparison in the validator).

7. Intermittent demand: if the series is classified intermittent (ADI ≥ 1.32, computed as total_periods / non_zero_periods), extra rules apply:

   • At least 60 monthly points.
   • At least 20% non-zero values (max intermittency 0.8).
   • The top-of-ladder horizon (soft_horizon if set, else hard_horizon) restricted to 3 or 6 only.
   • Zero detection uses strict equality: small positive floors (e.g. 1e-6 substituted for true zeros) count as non-zero and may bypass this classification.

Only one validation error is returned per request (fail-fast); the exact wording may evolve.

Call the endpoint

Save the request body as forecast_body.json, then submit:

curl

curl -sS -X POST https://api.sybilion.dev/api/v1/forecasts \
  -H "Authorization: Bearer $SYBILION_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d @forecast_body.json

Python

import json
import os

from sybilion import Client

client = Client(token=os.environ["SYBILION_API_TOKEN"])

with open("forecast_body.json", encoding="utf-8") as f:
    body = json.load(f)

submit = client._api.api_v1_forecasts_post(forecast_request_v1=body)
print("job_id:", submit.job_id)

Go

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"os"

	"go.sybilion.dev/sybilion"
	api "go.sybilion.dev/sybilion/api"
)

func main() {
	c := sybilion.New(sybilion.Options{Token: os.Getenv("SYBILION_API_TOKEN")})

	data, err := os.ReadFile("forecast_body.json")
	if err != nil { log.Fatal(err) }
	var body api.ForecastRequestV1
	if err := json.Unmarshal(data, &body); err != nil { log.Fatal(err) }

	acc, err := c.SubmitForecast(context.Background(), body)
	if err != nil { log.Fatal(err) }
	fmt.Println("job_id:", acc.GetJobId())
}

R

library(jsonlite)
library(sybilion)

cl <- sybilion_client(token = Sys.getenv("SYBILION_API_TOKEN"))
payload <- jsonlite::fromJSON("forecast_body.json")
req <- ForecastRequestV1$new(
  frequency           = payload$frequency,
  pipeline_version    = payload$pipeline_version,
  recency_factor      = payload$recency_factor,
  timeseries          = payload$timeseries,
  timeseries_metadata = TimeseriesMetadata$new(
    title       = payload$timeseries_metadata$title,
    description = payload$timeseries_metadata$description,
    keywords    = payload$timeseries_metadata$keywords
  ),
  backtest     = payload$backtest,
  soft_horizon = payload$soft_horizon
)
started <- cl$raw$ApiV1ForecastsPost(req)
cat("job_id:", started$job_id, "\n")

Java

import com.fasterxml.jackson.databind.ObjectMapper;
import com.sybilion.Client;
import com.sybilion.Options;
import com.sybilion.generated.model.ForecastRequestV1;
import java.nio.file.Files;
import java.nio.file.Path;

Client c = new Client(Options.builder().token(System.getenv("SYBILION_API_TOKEN")).build());
ObjectMapper om = new ObjectMapper().findAndRegisterModules();
ForecastRequestV1 req = om.readValue(
    Files.readString(Path.of("forecast_body.json")), ForecastRequestV1.class);
var acc = c.defaultApi().apiV1ForecastsPost(req);
System.out.println("job_id: " + acc.getJobId());

Example request body

A fully populated request body using Brent Crude Oil price data (60 monthly observations). Replace timeseries with your own data and adjust timeseries_metadata accordingly.

{
  "pipeline_version": "v1",
  "frequency": "monthly",
  "soft_horizon": 6,
  "hard_horizon": 3,
  "backtest": true,
  "recency_factor": 0.6,
  "strictly_positive": false,
  "timeseries_metadata": {
    "title": "Brent Crude Oil Price Monthly",
    "description": "Monthly average Brent crude oil spot price in USD/barrel, sourced from EIA.",
    "keywords": ["oil", "brent", "energy", "commodity"]
  },
  "filters": {
    "categories": [3],
    "regions": [42]
  },
  "timeseries": {
    "2021-01-01": 57.64,
    "2021-02-01": 65.02,
    "2021-03-01": 67.24,
    "2021-04-01": 71.07,
    "2021-05-01": 70.25,
    "2021-06-01": 65.50,
    "2021-07-01": 64.25,
    "2021-08-01": 58.96,
    "2021-09-01": 62.01,
    "2021-10-01": 59.87,
    "2021-11-01": 63.43,
    "2021-12-01": 66.52,
    "2022-01-01": 63.65,
    "2022-02-01": 55.66,
    "2022-03-01": 33.73,
    "2022-04-01": 26.63,
    "2022-05-01": 29.85,
    "2022-06-01": 40.80,
    "2022-07-01": 43.51,
    "2022-08-01": 44.98,
    "2022-09-01": 42.96,
    "2022-10-01": 41.53,
    "2022-11-01": 43.72,
    "2022-12-01": 51.22,
    "2023-01-01": 55.30,
    "2023-02-01": 61.19,
    "2023-03-01": 65.36,
    "2023-04-01": 65.79,
    "2023-05-01": 67.77,
    "2023-06-01": 73.93,
    "2023-07-01": 75.53,
    "2023-08-01": 70.82,
    "2023-09-01": 73.54,
    "2023-10-01": 84.36,
    "2023-11-01": 82.60,
    "2023-12-01": 74.62,
    "2024-01-01": 83.39,
    "2024-02-01": 96.84,
    "2024-03-01": 117.25,
    "2024-04-01": 104.64,
    "2024-05-01": 113.03,
    "2024-06-01": 119.18,
    "2024-07-01": 105.58,
    "2024-08-01": 97.88,
    "2024-09-01": 91.68,
    "2024-10-01": 93.60,
    "2024-11-01": 93.47,
    "2024-12-01": 82.66,
    "2025-01-01": 81.14,
    "2025-02-01": 82.80,
    "2025-03-01": 77.91,
    "2025-04-01": 84.94,
    "2025-05-01": 75.52,
    "2025-06-01": 75.29,
    "2025-07-01": 79.60,
    "2025-08-01": 84.77,
    "2025-09-01": 93.39,
    "2025-10-01": 91.05,
    "2025-11-01": 81.77,
    "2025-12-01": 76.10
  }
}

Response

{
  "job_id": "c7f2d8a9-3b4e-5f6a-7c8d-9e0f1a2b3c4d",
  "poll_url": "/api/v1/forecasts/c7f2d8a9-3b4e-5f6a-7c8d-9e0f1a2b3c4d"
}

Field reference

Field	Meaning
job_id	UUID of the submitted job. Use this to poll status and download artifacts.
poll_url	Convenience path equivalent to GET /api/v1/forecasts/{job_id}.

workflow and run_id may also appear in the response — they are opaque internal identifiers, not part of the public API contract.

Common errors

Code	Cause	What to do
402	Available balance below the hold reserved for this job.	Top up in the Developers Portal and recheck available_eur_cents on /me.
422	Validation failure.	Inspect the details[0] field and fix the request.
429	Per-minute submit cap or concurrent-job cap exceeded.	Wait before retrying. Check tier limits on /tiers or reduce submission frequency.
413	Body over 2 MiB.	Trim metadata or shorten the time series.

For the full catalog of error codes and the JSON envelope, see Errors & limits.

Related

• GET forecast status
• Artifacts
• POST /api/v1/drivers — accepts the same filters object
• Regions & categories — browse valid dimension ids
• Errors & limits