Time series & operations
Forecasting & anomaly detection
From sensor streams and operational data to early warnings and honest forecasts.
First working version in 6–10 weeks
The problem
Time-series projects fail quietly: models that look great in backtests and fall apart live, anomaly detectors that cry wolf, forecasts nobody dares use. The failure is almost always methodological — leakage, overfitting, evaluation that flatters.
Doing this honestly requires statistical discipline as much as machine learning: knowing what is predictable, what isn't, and proving which is which.
Our approach
Signal audit
We analyze your data's actual predictive content before promising anything — sometimes the honest answer is that a simpler statistical baseline wins.
Leak-proof evaluation design
Walk-forward validation, realistic baselines, and metrics tied to the decision the forecast supports.
Model development
From classical statistics to deep learning — chosen by evidence on your data, not fashion.
Anomaly detection with triage
Alerts ranked by severity and confidence, tuned to a false-positive rate your team will tolerate.
Production pipeline
Live data ingestion, monitoring for drift, and retraining strategy — engineered as software, not a notebook.
Reference architecture
- Data ingestion from sensors, databases, or market feeds
- Feature and signal-processing pipeline
- Forecasting and anomaly models with drift monitoring
- Backtesting and evaluation harness
- Alerting, dashboards, and API access
What you get
- An honest assessment of what your data can and cannot predict
- Models evaluated with leak-proof, walk-forward methodology
- A production pipeline, not a research notebook
- Monitoring that tells you when the model stops working
Proof. We have built research-to-production signal platforms for quantitative trading — the most adversarial forecasting environment there is — and bring that evaluation discipline to industrial data.
Find out what your data can actually predict.
A 30-minute call is enough to tell you whether this is feasible on your data.