Forecast what happens next — with models built for real decisions
We build forecasting capabilities into products and operational systems for demand, sales, capacity, inventory, usage and other signals that change over time. Models are backtested against historical data, integrated into the product, and monitored after release.
Where time-series forecasting creates value
We start with the decision, the forecast horizon and the cost of being wrong. From there, we build the smallest forecasting system that gives users a useful signal at the right time.
01
Demand & Inventory
Forecast demand by product, location or channel to support replenishment, inventory targets, procurement and availability decisions.
02
Capacity & Operations
Predict workload, traffic, energy use or resource demand so teams can plan capacity, staffing, maintenance and service levels before bottlenecks appear.
A forecast should support a decision — not just produce a number
We define the target and horizon, compare against simple baselines, expose uncertainty where it matters, and integrate the result into the workflow that actually uses the forecast.
03
Revenue & Commercial Planning
Forecast revenue, orders, cash-flow drivers or pipeline movement across products and markets to support planning and scenario analysis.
04
Product & Customer Activity
Predict usage, subscriptions, traffic, transactions or other product signals so the experience can adapt before demand changes become obvious.
What makes a forecast trustworthy
A useful forecast is not the most complex model. It is one that beats a simple baseline, is tested across time, makes uncertainty visible, and stays reliable after deployment.
Every candidate model is compared with simple historical and seasonal baselines. Complexity is justified only when it improves the metric that matters for the decision.
Beat a simple baseline
Naive → seasonal → statistical → ML
Every candidate model is compared with simple historical and seasonal baselines. Complexity is justified only when it improves the metric that matters for the decision.
Beat a simple baseline
Naive → seasonal → statistical → ML
Rolling
backtests
Evaluate performance across multiple historical cutoffs and forecast horizons instead of trusting one convenient train-test split.
Only information that would actually exist at forecast time may be used. Time-aware feature engineering prevents leakage from the future into training data.
Only information that would actually exist at forecast time may be used. Time-aware feature engineering prevents leakage from the future into training data.
Where the decision needs it, we expose prediction intervals or scenario ranges so users can see the uncertainty around a point forecast.
Monitored
in production
Track forecast error by horizon, segment and operating regime, and retrain or fall back when performance degrades.
Where the decision needs it, we expose prediction intervals or scenario ranges so users can see the uncertainty around a point forecast.
How we build a forecasting system
We treat forecasting as a product capability, not a notebook experiment. The work starts with the decision and ends with a monitored service that produces forecasts on the cadence your product needs.
01
Define target, horizon & decision
02
Build a time-aware dataset
03
Create temporal features & drivers
04
Establish baselines
Define exactly what the forecast will change
Define exactly what the forecast will change
Agree on the target, forecast horizon, update cadence, decision owner and cost of under- or over-forecasting. A demand forecast for tomorrow and a capacity forecast for next quarter are different products and should be evaluated differently.
Build the dataset as it would have existed in the past
Build the dataset as it would have existed in the past
Align timestamps, time zones, frequencies, missing periods and historical revisions. We record when each input actually became available so the training set does not quietly contain information from the future.
Create temporal features and external drivers
Create temporal features and external drivers
Build lags, rolling statistics, seasonal and calendar features, and add known drivers such as price, promotions, weather, holidays or capacity when they are available at prediction time. Feature availability is part of the model design.
Establish baselines before adding complexity
Establish baselines before adding complexity
Start with naive, seasonal and simple statistical baselines. They create a minimum standard for every more complex model and make it clear whether additional engineering is producing a meaningful improvement.
Production forecasting architecture
A production forecasting feature needs reliable time-aware data, repeatable evaluation, model serving and feedback from live performance. These components can run as one service or integrate with your existing data and ML platform.
01
Data Ingestion & Time Alignment
Purpose: create a reliable chronological dataset
Key components: source ingestion, timestamp and timezone alignment, resampling, missing-period handling, late-data rules
Key components: source ingestion, timestamp and timezone alignment, resampling, missing-period handling, late-data rules
02
Feature & Calendar Layer
Purpose: turn history and known future information into usable model inputs
Key components: lags, rolling windows, seasonality, holidays, promotions, prices and other forecast-time drivers
Key components: lags, rolling windows, seasonality, holidays, promotions, prices and other forecast-time drivers
03
Forecasting Engine
Purpose: generate forecasts with the appropriate model family
Key components: simple baselines, statistical models, gradient boosting, sequence models, model versioning
Key components: simple baselines, statistical models, gradient boosting, sequence models, model versioning
04
Backtesting & Evaluation
Purpose: prove performance before release
Key components: rolling-origin backtests, horizon-specific metrics, segment analysis, baseline comparison, interval coverage
Key components: rolling-origin backtests, horizon-specific metrics, segment analysis, baseline comparison, interval coverage
05
Serving & Product Integration
Purpose: make forecasts available where the product needs them
Key components: APIs, scheduled inference, output schemas, caching, fallbacks, feature freshness checks
Key components: APIs, scheduled inference, output schemas, caching, fallbacks, feature freshness checks
06
Monitoring & Retraining
Purpose: keep forecast quality visible after launch
Key components: data freshness, error tracking, drift detection, retraining triggers, model registry, rollback
Key components: data freshness, error tracking, drift detection, retraining triggers, model registry, rollback
05
Train candidate models
06
Backtest & calibrate uncertainty
07
Integrate into product workflow
08
Monitor, retrain & improve
Train candidate models for the structure in the data
Train candidate models for the structure in the data
Compare statistical models, tree-based machine learning and sequence models where appropriate. The choice depends on history length, number of related series, external drivers, latency requirements and how much complexity the product can support.
Backtest across time and calibrate uncertainty
Backtest across time and calibrate uncertainty
Use rolling historical cutoffs to test different horizons and operating periods. We compare error by segment, check stability, and calibrate prediction intervals when the user needs a range rather than a single point estimate.
Integrate the forecast into the product workflow
Integrate the forecast into the product workflow
Serve forecasts through APIs, scheduled jobs or data products and expose the result in the interface where the decision is made. We define fallbacks for missing inputs, stale data and unavailable models instead of treating serving as an afterthought.
Monitor forecast quality and retrain deliberately
Monitor forecast quality and retrain deliberately
Track data freshness, forecast error, drift, interval coverage, latency and business usage after release. Retraining follows measured degradation or a planned cadence, with the previous model available as a fallback during change.
Forecasting methods and engineering capabilities
We do not standardize on one forecasting algorithm. We compare simple baselines, statistical models and machine-learning approaches, then choose the smallest model family that performs reliably for the required horizon, latency and operating constraints.
Statistical Forecasting
Seasonal naive, exponential smoothing, ARIMA/SARIMA and related methods provide transparent baselines and can be strong production models when the series structure is stable.
Gradient Boosting for Time Series
XGBoost, LightGBM or CatBoost can combine lagged history, rolling features and external drivers when non-linear relationships matter more than a purely statistical time-series model.
Deep Learning & Sequence Models
Sequence models such as N-BEATS, temporal attention models and transformer-based approaches are considered when there is enough history, enough related series and a clear gain over simpler alternatives.
External Drivers & Multivariate Forecasts
Incorporate promotions, prices, weather, holidays, capacity or other known drivers while controlling when those variables are actually available to the forecast.
Hierarchical & Grouped Forecasts
Forecast related series across product, location, customer or business hierarchies and reconcile them where totals and lower-level forecasts need to remain consistent.
Probabilistic Forecasts & Prediction Intervals
Produce quantiles, prediction intervals or scenario ranges when users need to understand downside, upside or service-level risk instead of one point estimate.
Backtesting & Model Selection
Use rolling-origin evaluation, horizon-specific metrics and segment-level analysis to choose models based on repeated historical performance rather than one favorable split.
MLOps, Drift & Retraining
Version datasets, features and models; monitor data freshness and forecast error; define retraining triggers, approval rules and rollback paths for production changes.
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