When time is the dimension that matters
Not all forecasting problems are alike. When the variable you want to predict evolves over time — daily sales, hourly power consumption, an industrial furnace’s temperature every five minutes — you’re dealing with a time series problem. Treating it as a standard regression problem gives poor results.
Specialized time series models capture patterns that generic algorithms miss: long-term trends, daily or annual seasonality, holidays, calendar effects, and lagged correlations between variables. That makes them especially valuable for operational and financial planning.
Main use cases
Sales and demand forecasting
Accurate sales forecasts let you optimize inventory, plan production, and size your team precisely. Time series models draw on sales history, each item’s own seasonality, the effects of marketing campaigns, and external variables such as weather or local events. The result is a forecast by SKU, channel, and time horizon that’s significantly more accurate than classic statistical methods.
Energy consumption and cost optimization
For industrial companies on hourly electricity pricing, forecasting consumption in advance lets you shift loads to off-peak hours and cut the demand peaks that trigger penalties. LSTM models trained on consumption history and weather variables deliver high-resolution forecasts that support active energy management.
Anomaly detection in industrial processes
A machine that’s about to fail doesn’t change its behavior overnight. It drifts: the temperature rises a few degrees, the vibration pattern shifts, power draw moves. Time series anomaly detection models learn each machine’s normal behavior and raise alerts when they detect statistically significant deviations, so you can step in before the breakdown.
Financial markets and treasury
For treasury management and hedging currency or commodity risk, time series models provide scenario estimates that complement expert analysis. They don’t predict the future with certainty, but they quantify the probability distribution of different scenarios more rigorously than intuition can.
Models and methodology
Our model portfolio includes Prophet (robust to missing data and multiple seasonalities, excellent for business forecasting), LSTM and GRU networks (which capture long temporal dependencies in high-frequency data), and Temporal Fusion Transformers (state of the art for multivariate, multi-horizon forecasting). The choice depends on the forecast horizon, data frequency, and how much training data is available.
Every model comes with prediction intervals that communicate uncertainty honestly. A forecast without uncertainty is a false promise; one with well-calibrated intervals is a decision-making tool.
Integration into your workflows
Model output can be delivered through an interactive dashboard, a REST API that feeds your ERP or planning system directly, or an automated file in the format your team already uses. The goal is for the forecast to reach the decision-maker when and how they need it.
Frequently asked questions
How much history do I need for a reliable time series model?
As a rule of thumb, you need at least two or three full cycles of the pattern you want to model. For annual seasonality, at least two years. For weekly patterns, several months. With less data, Bayesian techniques and simpler models can still give useful results, though with more uncertainty.
What happens when the history has gaps or errors?
Real data is always imperfect. The preprocessing pipeline detects and handles missing values, outliers, and structural breaks (such as a COVID shutdown or a system change). Ignoring these events distorts the model; documenting and handling them properly is part of the job.
How far ahead can the model forecast?
The useful horizon depends on the problem. For consumer demand, a week or a month is typical. For predictive maintenance, it depends on how long it takes to schedule the intervention. We always calibrate the model for the horizon that has real operational value, not the one that yields the lowest error on paper.