Use Cases — TSFM.ai

Use cases

See how TSFMs apply across forecasting, anomaly detection, classification, and domain-specific workflows.

Supported tasks

Forecasting

/v1/forecast

The primary use case. Given historical observations, predict future values with uncertainty estimates. TSFMs produce point forecasts, quantile intervals, and full predictive distributions depending on the model.

Examples

Demand planning — forecast next-week unit sales per SKU to optimize inventory allocation
Energy load forecasting — predict hourly electricity consumption for grid balancing
Financial projections — forecast revenue run rate with confidence intervals for board reporting
Capacity planning — predict server request volumes to right-size infrastructure

Recommended models: All models on TSFM.ai support forecasting. Chronos-Bolt and TimesFM 2.0 are popular starting points.

Anomaly detection

/v1/detect-anomalies

Identify unusual observations that deviate significantly from expected patterns. The model learns what 'normal' looks like from the series history and flags data points that fall outside the expected distribution.

Examples

Infrastructure monitoring — detect CPU spikes, memory leaks, and network anomalies in real-time
Fraud detection — flag unusual transaction patterns in payment systems
Quality control — identify sensor readings that indicate manufacturing defects
Security — detect unusual access patterns in application logs

Recommended models: Forecast-based anomaly detection works with any hosted model. Use chronos-bolt-base or moirai-1.1-R-base to generate prediction intervals and flag observations outside expected ranges.

Classification

/v1/classify

Categorize time series into predefined classes based on their temporal characteristics. Useful for pattern recognition, regime detection, and automated labeling of signal types.

Examples

Regime detection — classify market conditions as trending, mean-reverting, or volatile
Sensor type identification — automatically categorize IoT sensor streams by signal type
Activity recognition — classify user behavior patterns from interaction timelines
Failure mode classification — identify which type of equipment failure a vibration signal indicates

Recommended models: Classification typically requires fine-tuning. Pre-trained TSFMs can extract temporal features that feed into a downstream classifier.

Imputation

/v1/impute

Fill in missing values in time series data while preserving statistical properties. TSFMs understand the expected patterns and can reconstruct gaps that are consistent with the surrounding context.

Examples

Sensor data gaps — fill in missing readings from temporarily offline sensors
Survey data — interpolate missing responses while maintaining distributional properties
Historical data cleaning — reconstruct corrupted or incomplete historical records
Multi-source alignment — fill gaps when merging data from sources with different reporting schedules

Recommended models: A forecast-based approach can approximate imputation with any hosted model. Generate forecasts across the gap and use the predicted values to fill missing observations.

Industry applications

TSFMs are being adopted across industries. Here is how organizations are applying them today.

Retail and e-commerce

Demand forecasting

Predict unit sales per SKU at store level for replenishment and markdown optimization

Promotion impact

Model the lift from planned promotions using future covariates

New product launch

Zero-shot forecasting for items with no sales history using transfer from similar categories

Energy and utilities

Load forecasting

Predict hourly and daily electricity demand for grid operations and trading

Renewable generation

Forecast solar and wind output to balance supply and demand

Anomaly detection

Identify unusual consumption patterns indicating equipment issues or theft

Finance and insurance

Revenue forecasting

Project financial metrics with prediction intervals for scenario planning

Risk modeling

Quantify tail risk using probabilistic forecast distributions

Claims volume

Predict insurance claim volumes by category for reserve allocation

Infrastructure and DevOps

Capacity planning

Forecast request volumes and resource utilization for auto-scaling

Incident detection

Detect anomalous latency, error rate, and throughput patterns

Cost forecasting

Predict cloud spend trends for budget planning and optimization

Healthcare and life sciences

Patient volume

Forecast emergency department and clinic visit volumes for staffing

Clinical monitoring

Detect anomalous vital sign patterns in continuous patient monitoring

Drug demand

Predict pharmaceutical demand by region to prevent stockouts

Manufacturing and supply chain

Production planning

Forecast demand upstream to optimize production schedules

Predictive maintenance

Detect degradation patterns in equipment sensor data before failures

Logistics optimization

Predict shipping volumes and transit times for route planning

When TSFMs may not be the best fit

TSFMs are powerful but not universally optimal. Consider alternatives when:

You have a single, well-understood series with decades of clean history — a tuned ARIMA or ETS may suffice and be cheaper to run.
Your data has strong domain-specific structure that requires custom feature engineering — tree-based models (LightGBM, XGBoost) may capture engineered features better.
You need sub-millisecond latency — even fast TSFMs add 50-100ms of GPU inference time. Simple statistical methods can run in microseconds.
Your series is fundamentally unpredictable (pure random walk) — no model, classical or foundation, will produce meaningful forecasts.

Next steps

Choosing a model — Use a decision framework to select the right TSFM based on your latency, cost, and accuracy requirements.

Quickstart — Ready to try it? Make your first forecast API call in under 5 minutes.