reuse_forecasting_agent_and_get_forecast
Generate forecasts for values between 0 and 1 using a NormalizedForecaster agent. Specify input data, forecast start date, time scale, and horizon length to receive predictions and text explanations. Integrates with Chronulus for AI-powered forecasting.
Instructions
This tool creates a NormalizedForecaster agent with your session and input data model and then provides a forecast input data to the agent and returns the prediction data and text explanation from the agent.
When to use this tool:
- Use this tool to request a forecast from Chronulus
- This tool is specifically made to forecast values between 0 and 1 and does not require historical data
- The prediction can be thought of as seasonal weights, probabilities, or shares of something as in the decimal representation of a percent
How to use this tool:
- First, make sure you have a session_id for the forecasting or prediction use case.
- Next, think about the features / characteristics most suitable for producing the requested forecast and then create an input_data_model that corresponds to the input_data you will provide for the thing being forecasted.
- Remember to pass all relevant information to Chronulus including text and images provided by the user.
- If a user gives you files about a thing you are forecasting or predicting, you should pass these as inputs to the
agent using one of the following types:
- ImageFromFile
- List[ImageFromFile]
- TextFromFile
- List[TextFromFile]
- PdfFromFile
- List[PdfFromFile]
- If you have a large amount of text (over 500 words) to pass to the agent, you should use the Text or List[Text] field types
- Finally, add information about the forecasting horizon and time scale requested by the user
- Assume the dates and datetimes in the prediction results are already converted to the appropriate local timezone if location is a factor in the use case. So do not try to convert from UTC to local time when plotting.
- When plotting the predictions, use a Rechart time series with the appropriate axes labeled and with the prediction explanation displayed as a caption below the plot
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent_id for the forecasting or prediction use case and previously defined input_data_model | |
| forecast_start_dt_str | Yes | The datetime str in '%Y-%m-%d %H:%M:%S' format of the first value in the forecast horizon. | |
| horizon_len | No | The integer length of the forecast horizon. Eg., 60 if a 60 day forecast was requested. | |
| input_data | Yes | The forecast inputs that you will pass to the chronulus agent to make the prediction. The keys of the dict should correspond to the InputField name you provided in input_fields. | |
| time_scale | No | The times scale of the forecast horizon. Valid time scales are 'hours', 'days', and 'weeks'. | days |