| id | tsfm_forecast_sensor |
| name | TSFM Forecast Sensor |
| version | 1.0.0 |
| description | Runs TSFM forecasting on a provided time-series dataset for explicit target columns. |
| required_servers | ["tsfm"] |
| asset_types | ["chiller","equipment","time_series"] |
| keywords | ["tsfm","forecast","forecasting","ttm","time series"] |
| default_enabled | true |
| inputs | {"dataset_path":{"type":"string","required":true},"timestamp_column":{"type":"string","required":true},"target_columns":{"type":"array","required":true},"model_checkpoint":{"type":"string","required":false},"forecast_horizon":{"type":"integer","required":false}} |
| execution | {"type":"declarative"} |
TSFM Forecast Sensor
Use this skill when the user provides or clearly references an existing time-series dataset and asks to forecast one or more target sensor columns using a TSFM or TTM model.
Do not use this skill when the user first needs IoT data retrieved from CouchDB, when the query asks for anomaly detection, or when the target/timestamp columns are missing.
Workflow
- Run zero-shot TSFM forecasting on the provided dataset.
- Use the requested model checkpoint when specified; otherwise use the TSFM server default.
- Return the forecast output file and relevant status message.
Expected Summary
The final answer should state:
- The dataset path, timestamp column, and target columns used.
- The model checkpoint and forecast horizon when provided.
- The
results_file path returned by the TSFM server.
- Any TSFM error message if forecasting could not be completed.
Execution Plan
{
"steps": [
{
"name": "forecast",
"server": "tsfm",
"tool": "run_tsfm_forecasting",
"arguments": {
"dataset_path": "$dataset_path",
"timestamp_column": "$timestamp_column",
"target_columns": "$target_columns",
"model_checkpoint": "$model_checkpoint",
"forecast_horizon": "$forecast_horizon"
}
}
]
}