Group rows before forecasting

Your table is one row per store per day, but you want a forecast per city — every store in that city summed together. Declare the grouping in hierarchies, then pick the level you want with selector:

Code
{
  "input": {
    "source": {
      "inline": {
        "date":    ["2024-01-01", "2024-01-01", "2024-01-01", "2024-01-02", "2024-01-02", "2024-01-02"],
        "store":   ["S1", "S2", "S1", "S1", "S2", "S1"],
        "city":    ["Paris", "Lyon", "Paris", "Paris", "Lyon", "Paris"],
        "channel": ["online", "online", "in_store", "online", "online", "in_store"],
        "sales":   [42, 30, 58, 40, 28, 62]
      }
    },
    "columns": {
      "date":    { "kind": "time", "frequency": "1d" },
      "store":   "identifier",
      "city":    "identifier",
      "channel": "identifier",
      "sales":   { "kind": "target", "aggregate": "sum" }
    },
    "hierarchies": {
      "store": [["store", "city"]]
    }
  },
  "selector": { "city": "any" },
  "prediction_length": "1w"
}

Post it to https://api.retrocast.com/v1-beta/forecast?model=t0-alpha. model names the forecasting model and is required — without it the request is rejected with missing field "model".

What each part does:

• hierarchies says store sits inside city.
• selector: { "city": "any" } asks for one forecast per city. Every store in a city is summed into it.
• channel appears in neither hierarchies nor selector, so it is summed over too — online and in_store are added together.

To get fewer series back, name the values instead of "any": { "city": ["Paris"] } returns Paris only, still summed across its stores and channels.

You don't need to reshape your table or keep a separate rollup table. Send the rows you have and pick the level at request time.

What comes back

One row per (city, time step) — two cities over seven days, so 14 rows, trimmed here to the first two:

Code
{
  "data": {
    "time":      ["2024-01-03T00:00:00Z", "2024-01-04T00:00:00Z"],
    "cutoff":    ["2024-01-02T00:00:00Z", "2024-01-02T00:00:00Z"],
    "lead_time": ["PT0S", "P1D"],
    "span":      ["P1D", "P1D"],
    "identifiers": { "city": ["Lyon", "Lyon"] },
    "targets": {
      "sales": {
        "quantiles": {
          "0.1":  [26.64, 26.68],
          "0.5":  [28.48, 28.71],
          "0.9":  [31.47, 32.40],
          "mean": [28.68, 29.00]
        }
      }
    }
  }
}

identifiers carries only the dimensions that identify a row — here city alone. store and channel are absent: those rows were summed together, so there is no single value to report.

The request above sets no quantiles, so the response comes back with a default spread that includes mean. Set quantiles yourself and you get exactly the levels you asked for.