--- name: time-series-forecaster-3a85a5ed183d description: Time series forecasting skill for business metric prediction and demand planning allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: decision-intelligence domain: business category: forecasting priority: medium shared-candidate: true tools-libraries: - prophet - statsforecast - darts - sktime - nixtla graph: domains: [domain:business-intelligence] skillAreas: [skill-area:statistical-analysis, skill-area:data-analytics, skill-area:quantitative-modeling] roles: [role:data-scientist, role:data-analyst, role:analytics-engineer] --- # 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 ```python # 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 ```python # 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 ```python # 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 ```json { "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 ```json { "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 1. Use at least 2-3 full seasonal cycles of historical data 2. Check for and handle missing values appropriately 3. Consider external factors (holidays, promotions, economic indicators) 4. Validate with time series cross-validation (not random split) 5. Report prediction intervals, not just point forecasts 6. Monitor forecast accuracy over time and retrain as needed 7. 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 --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Marketing, Product Management, Sales Roles: Business Development, Data Scientist, Growth Marketer, Marketing Analyst, Product Manager Specialties: Specialty not specified Tools: Darts, Nixtla, Prophet, Sktime, Statsforecast Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 90feabe46224cffa939e2ce27cf5314875c9a2587118db6cd842d1be68649929