Does it provide model evaluation metrics?

Yes, it generates comprehensive reports including MAPE, RMSE, MASE, and directional accuracy to evaluate how well the model performs on unseen data.

What forecasting methods are supported?

The skill supports statistical baselines (ARIMA), business-focused models (Facebook Prophet), deep learning (LSTM/GRU), and multivariate models (VAR).

How do I integrate forecasts into my existing workflow?

The skill uses the SpecWeave increment system to store models, forecasts, and analysis reports in a structured directory, making it easy to sync with your documentation and version control.

How does it handle data leakage in time-series?

Unlike standard ML, this skill enforces temporal order by using chronological train/test splits and expanding window cross-validation to ensure models never 'see' the future during training.

Can I include external factors like holidays or marketing spend?

Yes, the Prophet and Multivariate forecasters allow you to add custom regressors and built-in holiday effects to improve prediction accuracy.

Time Series Forecaster

Name: Time Series Forecaster
Author: anton-abyzov

byanton-abyzov

Ciencia de Datos y ML

Automates complex time-series forecasting pipelines including trend analysis, seasonality detection, and multi-model predictions.

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Time Series Forecaster is a specialized module for Claude Code designed to build, evaluate, and deploy forecasting models for time-dependent data. It provides structured workflows for statistical methods like ARIMA, business-centric models like Facebook Prophet, and deep learning architectures like LSTM. By integrating with the SpecWeave increment system, it ensures that ML experiments are reproducible and well-documented. The skill handles the critical nuances of time-series data, such as temporal splitting to prevent data leakage, stationarity testing, and holiday-aware seasonality, making it ideal for developers building predictive analytics into their applications.

Características Principales

Automated stationarity testing (ADF) and seasonal decomposition (Trend/Season/Residual)
Multivariate forecasting capabilities to incorporate external regressors and related time series
Seamless SpecWeave integration for managing model versions, experiments, and evaluation reports
Temporal cross-validation with expanding windows to ensure model stability
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Multi-model support including ARIMA, Prophet, LSTM, and Vector AutoRegression (VAR)

Casos de Uso

Financial trend analysis and volatility forecasting for stock or crypto markets
Predictive maintenance and anomaly detection for IoT sensor data and machine metrics
Sales and demand forecasting for e-commerce and inventory management

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Características Principales

Automated stationarity testing (ADF) and seasonal decomposition (Trend/Season/Residual)
Multivariate forecasting capabilities to incorporate external regressors and related time series
Seamless SpecWeave integration for managing model versions, experiments, and evaluation reports
Temporal cross-validation with expanding windows to ensure model stability
0 GitHub stars
Multi-model support including ARIMA, Prophet, LSTM, and Vector AutoRegression (VAR)

Casos de Uso

Financial trend analysis and volatility forecasting for stock or crypto markets
Predictive maintenance and anomaly detection for IoT sensor data and machine metrics
Sales and demand forecasting for e-commerce and inventory management