time-series-forecaster

// Time series forecasting skill for business metric prediction and demand planning

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updated:March 4, 2026

SKILL.mdreadonly

SKILL.md Frontmatter

nametime-series-forecaster

descriptionTime series forecasting skill for business metric prediction and demand planning

allowed-toolsRead,Write,Glob,Grep,Bash

metadata[object Object]

Time Series Forecaster

Overview

The Time Series Forecaster skill provides comprehensive capabilities for predicting business metrics over time using classical statistical methods, machine learning, and deep learning approaches. It supports automated model selection, ensemble forecasting, and uncertainty quantification for robust business planning.

Capabilities

Classical methods (ARIMA, ETS, Theta)
Machine learning methods (XGBoost, LightGBM for time series)
Deep learning methods (Prophet, N-BEATS, Temporal Fusion Transformer)
Ensemble forecasting
Prediction interval generation
Forecast accuracy metrics (MAPE, RMSE, MASE)
Anomaly detection
Seasonality decomposition

Used By Processes

Predictive Analytics Implementation
KPI Framework Development
Market Sizing and Opportunity Assessment

Usage

Data Input

# Time series data configuration
time_series_data = {
    "target": "monthly_revenue",
    "datetime_column": "date",
    "frequency": "M",  # Monthly
    "data": [
        {"date": "2023-01-01", "value": 1000000, "marketing_spend": 50000},
        {"date": "2023-02-01", "value": 1050000, "marketing_spend": 55000},
        # ... more data
    ],
    "exogenous_variables": ["marketing_spend", "economic_index"],
    "special_events": [
        {"date": "2023-11-24", "event": "black_friday", "impact": "positive"},
        {"date": "2023-12-25", "event": "christmas", "impact": "mixed"}
    ]
}

Model Configuration

# Forecasting configuration
forecast_config = {
    "horizon": 12,  # 12 months ahead
    "models": {
        "auto_select": True,
        "candidates": ["arima", "ets", "prophet", "lightgbm"],
        "ensemble": {
            "method": "weighted_average",
            "weights": "based_on_cv_performance"
        }
    },
    "validation": {
        "method": "time_series_cv",
        "n_splits": 5,
        "test_size": 3
    },
    "prediction_intervals": [0.50, 0.80, 0.95]
}

Seasonality Analysis

# Seasonality decomposition
seasonality_config = {
    "method": "stl",  # or "classical", "x13"
    "seasonal_periods": [12],  # yearly for monthly data
    "robust": True,
    "output_components": ["trend", "seasonal", "residual"]
}

Model Selection Guide

Model	Best For	Handles
ARIMA	Stationary data with autocorrelation	Trend, AR/MA patterns
ETS	Exponential patterns	Trend, Seasonality, Error
Prophet	Business time series	Trend, Multiple seasonality, Holidays
Theta	Simple forecasting	Trend extrapolation
N-BEATS	Complex patterns	Non-linear trends, Interpretable
TFT	Multi-horizon, multivariate	Exogenous vars, Attention
XGBoost	Feature-rich forecasting	Exogenous variables

Accuracy Metrics

Metric	Formula	Use Case
MAPE	Mean Absolute Percentage Error	Scale-independent comparison
RMSE	Root Mean Square Error	Penalizes large errors
MASE	Mean Absolute Scaled Error	Compares to naive forecast
SMAPE	Symmetric MAPE	Handles near-zero values
Coverage	% in prediction interval	Calibration check

Input Schema

{
  "time_series": {
    "target": "string",
    "datetime_column": "string",
    "frequency": "string",
    "data": ["object"],
    "exogenous_variables": ["string"]
  },
  "forecast_config": {
    "horizon": "number",
    "models": "object",
    "validation": "object",
    "prediction_intervals": ["number"]
  },
  "analysis_options": {
    "decomposition": "boolean",
    "anomaly_detection": "boolean",
    "feature_importance": "boolean"
  }
}

Output Schema

{
  "forecasts": {
    "point_forecast": ["number"],
    "prediction_intervals": {
      "lower_80": ["number"],
      "upper_80": ["number"],
      "lower_95": ["number"],
      "upper_95": ["number"]
    },
    "dates": ["string"]
  },
  "model_performance": {
    "selected_model": "string",
    "cv_metrics": {
      "MAPE": "number",
      "RMSE": "number",
      "MASE": "number"
    },
    "all_models": "object"
  },
  "decomposition": {
    "trend": ["number"],
    "seasonal": ["number"],
    "residual": ["number"]
  },
  "anomalies": [
    {
      "date": "string",
      "value": "number",
      "expected": "number",
      "severity": "string"
    }
  ],
  "feature_importance": "object (if applicable)"
}

Best Practices

Use at least 2-3 full seasonal cycles of historical data
Check for and handle missing values appropriately
Consider external factors (holidays, promotions, economic indicators)
Validate with time series cross-validation (not random split)
Report prediction intervals, not just point forecasts
Monitor forecast accuracy over time and retrain as needed
Be cautious with long-horizon forecasts (uncertainty compounds)

Integration Points

Feeds into KPI Tracker for forward-looking metrics
Connects with Monte Carlo Engine for scenario analysis
Supports Predictive Analyst agent
Integrates with Decision Visualization for forecast charts