Time Series Forecasting Specialist

Designs and implements time series forecasting solutions — from data preprocessing and stationarity testing through model selection (ARIMA, Prophet, LSTM, Transformer-based), evaluation with proper temporal cross-validation (MAPE, RMSE, MASE), to production deployment with ensemble approaches and drift monitoring.

Gold

v1.0.00 activationsData & AnalyticsTechnologyadvanced

SupaScore

84.6

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.5

Best for

▸Multi-step-ahead demand forecasting for inventory planning with Prophet and ARIMA models
▸Stock price prediction using LSTM networks with proper temporal cross-validation
▸Energy consumption forecasting with seasonal decomposition and ensemble methods
▸Sales revenue forecasting with uncertainty quantification and drift monitoring
▸Supply chain demand planning with multiple seasonality detection and MASE evaluation

What you'll get

●Complete Python forecasting pipeline with STL decomposition, stationarity testing, multiple model comparison (ARIMA, Prophet, LSTM), and temporal cross-validation results
●Production deployment architecture with model serving, drift monitoring, ensemble weighting, and automated retraining triggers
●Comprehensive model evaluation report with MAPE, RMSE, MASE metrics across different forecast horizons, residual analysis, and business impact assessment

Not designed for ↓

×Real-time anomaly detection or fraud detection (use anomaly detection specialist instead)
×Cross-sectional prediction problems without temporal dependencies
×Causal inference or A/B testing analysis (correlation vs causation)
×General machine learning classification tasks on non-temporal data

Expects

Historical time series data with consistent timestamps, clear forecast horizon, and business context about seasonal patterns and external factors.

Returns

Production-ready forecasting pipeline with model comparison, uncertainty intervals, evaluation metrics (MAPE, RMSE, MASE), and monitoring setup.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

time-seriesforecastingarimasarimaprophetlstmtransformerstationarityseasonal-decompositioncross-validationmapermsemaseensemble-methods

Research Foundation: 8 sources (3 paper, 1 books, 2 academic, 2 official docs)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

Data Pipeline ArchitectGold Demand Forecasting AnalystGold Inventory Forecasting AnalystGold Python Data AnalystGold Statistical Analysis AdvisorPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

PyTorch Deep Learning EngineerPlatinum MLOps Platform EngineerGold Anomaly Detection SpecialistGold

Common Workflows

End-to-End Demand Forecasting Pipeline

Complete workflow from data exploration through forecasting model development to production deployment with monitoring

Python Data Analyst→time-series-forecasting-specialist→MLOps Platform Engineer

Activate this skill in Claude Code

Start Free to Activate This Skill