Data & AI Engineering

Time series forecasting for real planning decisions

We develop forecasting systems for demand, energy consumption and operational loads. Each model must improve a defined planning decision and withstand backtesting, changing conditions and comparison with simple baselines.

Discuss the current situation

The operational problem

Planning with basic historical averages may miss seasonality and exceptional events, but complex machine-learning models can also underperform simple baselines.

We define the prediction horizon, available signals and cost of over- or underestimation before choosing a model. A forecast only matters when a real decision changes because of it.

What we engineer

Demand and inventory forecasts

Predict volumes and inventory needs across useful planning horizons.

Energy and load forecasting

Model consumption, load and available external features such as weather or calendar effects.

Capacity planning

Support operational resources and utilization with uncertainty ranges.

Feature engineering

Build time-aware features without leaking information from the future.

Backtesting and monitoring

Measure forecast errors across time windows, segments and production drift.

Methods and trade-offs

Baseline models, including seasonal naive forecasts, should be compared with statistical and ML approaches. Random row splits are generally unsuitable for time-series evaluation.

Prediction intervals and business costs matter as much as headline metrics. Only data known at prediction time may be used. We monitor quality per site, product or cohort.

From evaluation to production

Define the decision

Set horizon, granularity, target and error costs.

Inspect historical data

Assess missing values, seasonality, external information and anomalies.

Backtest candidates

Compare baseline, statistical and ML models on rolling time windows.

Connect to planning

Expose forecasts and uncertainty through APIs or planning tools.

Monitor and retrain carefully

Track errors, drift and changes to source feeds.

Frequently asked questions

How much history is needed?

It depends on seasonality, horizon and variability. We test coverage and quality rather than assuming a fixed minimum.

Is ML always better?

No. Seasonal or statistical models can be more robust, explainable and economical.

How do you prevent data leakage?

With time-based backtests and strict checks that features were available at forecast time.

How are forecasts maintained?

By tracking errors, drift, model versions and relevant data-source changes.

Technical discovery

Discuss the system and implementation constraints

We review the existing systems, technical constraints and a measurable first delivery step.