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Analyze a time series and generate forecasts with confidence intervals. Use when predicting future values from historical data.
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Baseado na classificação ocupacional SOC
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| name | forecast |
| description | Analyze a time series and generate forecasts with confidence intervals. Use when predicting future values from historical data. |
| allowed-tools | mcp__localdata__execute_query mcp__localdata__analyze_time_series mcp__localdata__forecast_time_series |
| argument-hint | <database-name> <column-name> |
Decompose a time series, test for stationarity, and generate forecasts with confidence intervals.
Parse arguments. Extract the database name and target column name from $ARGUMENTS. The first argument is the database name, the second is the column to forecast.
Query the time series. Call execute_query to select the datetime column and the target column, ordered by time ascending. If the table is not obvious, query the schema first. Ensure no gaps in the time index. Limit to the most recent 10,000 observations if the series is very long.
Analyze the series. Call analyze_time_series with the database name and column. Review the decomposition results:
Interpret the analysis. Summarize the time series characteristics:
Generate forecasts. Call forecast_time_series with the database name, column, and desired forecast horizon. The tool will select an appropriate model (ARIMA, ETS, or SARIMA) based on the series characteristics. Request confidence intervals at the 80% and 95% levels.
Present the forecast. Display results in a structured format:
Assess forecast reliability. Comment on:
Recommend next steps. Suggest monitoring actual values against forecasts, re-fitting when new data arrives, or running /localdata-mcp:analyze-correlations to find external predictors that could improve the model.
