Time Series Forecasting Expert

Provides expert guidance on time series forecasting including statistical methods (ARIMA, ETS), Prophet, neural forecasting (N-BEATS, Temporal Fusion Transformer), seasonality decomposition, anomaly detection, and confidence interval estimation.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

85.3

Research Quality (15%)

8.7

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.6

Best for

▸Demand forecasting for inventory planning with seasonal patterns and external regressors
▸Financial time series prediction with uncertainty quantification for budget planning
▸Anomaly detection in operational metrics with automated alerting thresholds
▸Multi-step ahead forecasting for capacity planning in manufacturing or cloud infrastructure
▸Prophet model implementation for business metrics with holiday effects and changepoint detection

What you'll get

●Comprehensive forecasting pipeline with model selection rationale, hyperparameter tuning results, and production deployment considerations
●Statistical decomposition analysis with visualizations showing trend, seasonal, and residual components plus forecast accuracy metrics
●Neural forecasting implementation with architecture details, training procedures, and comparison against statistical baselines

Not designed for ↓

×Real-time streaming predictions requiring sub-second latency
×Cross-sectional data analysis or classification problems without temporal structure
×Causal inference or determining why patterns occur in time series
×Image or text sequence modeling despite being sequential data

Expects

Historical time series data with clear frequency, sufficient length (50+ observations for statistical methods, 1000+ for neural), and context about seasonality, external factors, and business constraints.

Returns

Point forecasts with prediction intervals, model performance metrics, seasonality decomposition analysis, and recommendations for model selection with implementation code.

Evidence Policy

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

time-seriesforecastingarimaprophetn-beatstemporal-fusion-transformerseasonalityanomaly-detectionprediction-intervalsdemand-forecastingneural-forecasting

Research Foundation: 8 sources (2 books, 6 paper)

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 Statistical Analysis AdvisorPlatinum

Works well with

Anomaly Detection SpecialistGold Dashboard Design StrategistGold Demand 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 ML Model Evaluation ExpertGold Real-Time Feature Serving ExpertGold

Common Workflows

Production Forecasting System

End-to-end forecasting system from model development through production monitoring with automated retraining

time-series-forecasting-expert→ML Model Evaluation Expert→model-deployment-optimizer→monitoring-observability-designer

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