{
  "type": "object",
  "properties": {
    "mode": {
      "type": "string",
      "enum": [
        "route",
        "ensemble",
        "explicit"
      ],
      "description": "\"route\": Ephemeris picks the best model for the data, falling back to a small ensemble if it fails. \"ensemble\": run all compatible models and blend them; best calibration, highest cost. \"explicit\": run the single model named in `model`."
    },
    "model": {
      "description": "Required when mode is \"explicit\". Must be a name returned by list_models.",
      "type": "string",
      "minLength": 1
    },
    "series": {
      "minItems": 1,
      "maxItems": 64,
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "values": {
            "anyOf": [
              {
                "minItems": 1,
                "type": "array",
                "items": {
                  "type": "number"
                }
              },
              {
                "minItems": 1,
                "type": "array",
                "items": {
                  "minItems": 1,
                  "type": "array",
                  "items": {
                    "type": "number"
                  }
                }
              }
            ],
            "description": "Observed history, oldest first. A flat number array is one univariate series. A nested array is one multivariate series with one inner array per variate, all the same length."
          },
          "freq": {
            "description": "Pandas-style sampling frequency such as \"H\", \"D\", \"W\", \"15min\" or \"M\". Improves routing and seasonal handling; omit if unknown.",
            "type": "string",
            "minLength": 1
          },
          "covariates": {
            "type": "object",
            "properties": {
              "past": {
                "type": "object",
                "propertyNames": {
                  "type": "string"
                },
                "additionalProperties": {
                  "type": "array",
                  "items": {
                    "type": "number"
                  }
                },
                "description": "Named historical channels, each the same length as the series context."
              },
              "future": {
                "description": "Known-future channels, each exactly `horizon` long. Every future channel must also appear in `past`.",
                "type": "object",
                "propertyNames": {
                  "type": "string"
                },
                "additionalProperties": {
                  "type": "array",
                  "items": {
                    "type": "number"
                  }
                }
              }
            },
            "required": [
              "past"
            ],
            "description": "Optional exogenous covariates. Only models with `covariates: true` in list_models can use them. In route and ensemble mode the panel narrows to those models; in explicit mode, naming a model without covariate support is an error."
          }
        },
        "required": [
          "values"
        ]
      },
      "description": "One to 64 series forecast in one request. All share horizon and quantiles."
    },
    "horizon": {
      "description": "Number of future steps to forecast, 1 to 4096. Defaults to 64. Some models stop short of this (max_horizon in list_models): route and ensemble skip them, explicit mode rejects the request.",
      "type": "integer",
      "minimum": 1,
      "maximum": 4096
    },
    "context_len": {
      "description": "Most recent points per variate to feed the model and bill for, 1 to 16384. Defaults to 256. Values beyond the model's context cap are truncated at the cap.",
      "type": "integer",
      "minimum": 1,
      "maximum": 16384
    },
    "quantiles": {
      "description": "Up to 21 quantile levels strictly between 0 and 1, for example [0.1, 0.5, 0.9]. The response keys forecasts by these as decimal strings.",
      "maxItems": 21,
      "type": "array",
      "items": {
        "type": "number",
        "exclusiveMinimum": 0,
        "exclusiveMaximum": 1
      }
    },
    "top_k": {
      "description": "Ensemble only: cap on how many models participate.",
      "type": "integer",
      "minimum": 1,
      "maximum": 16
    },
    "combine": {
      "description": "Ensemble only. \"mixture\" averages the predictive distributions (default); \"vincentize\" averages the quantiles.",
      "type": "string",
      "enum": [
        "mixture",
        "vincentize"
      ]
    },
    "idempotency_key": {
      "description": "Client-chosen key, 8 to 128 characters of letters, numbers, dot, underscore, colon or hyphen. A retry with the same key and identical body replays the stored result without charging again.",
      "type": "string",
      "pattern": "^[A-Za-z0-9._:-]{8,128}$"
    }
  },
  "required": [
    "mode",
    "series"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}

{
  "type": "object",
  "properties": {
    "limit": {
      "description": "Rows per page, 1 to 200. Defaults to 50.",
      "type": "integer",
      "minimum": 1,
      "maximum": 200
    },
    "offset": {
      "description": "Rows to skip. Use `pagination.next_offset` from the previous page.",
      "type": "integer",
      "minimum": 0,
      "maximum": 9007199254740991
    }
  },
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}