# Forecasting & Machine Learning

> Forecasting and machine learning use your historical data to predict what happens next (demand, churn, risk, failures, prices), and to score and rank cases, using the right technique for the problem, from classical statistical models to deep learning, rather than defaulting to whatever is fashionable.

_Source: https://plenaura.com/services/forecasting-machine-learning · Last updated: 2026-06-03 · Plenaura_

**Outcome:** Decisions about stock, staffing, cash, and risk get made against a clear view of what's coming, grounded in your own data instead of gut feel and last year's spreadsheet.

## What you get

- A forecasting or scoring model built on your historical data, validated honestly
- The right technique for the problem, classical ML or deep learning, chosen on merit
- Clear accuracy expectations, including where the model is and isn't reliable
- Integration so predictions reach the people and systems that act on them
- The models, code, and documentation handed over, yours to run and retrain

## This is for you if

- You plan stock, staffing, or cash on gut feel and last year's spreadsheet
- You want to know which customers will churn, or which leads will convert
- You need to spot fraud, risk, or failures before they cost you
- You have years of data but nothing turning it into a forward view

## FAQ

### How much data do we need for this to work?

Less than most people assume. A useful forecast can often be built on a couple of years of ordinary business history, and some scoring problems need surprisingly little. Before we commit, we look at your data and tell you honestly what it can support, and where it's too thin, we say so instead of building something that looks confident and isn't.

### Isn't a neural network always the best model?

No, and anyone who says otherwise is selling something. For a lot of business forecasting, a well-chosen statistical model beats a deep neural network: it's more accurate on limited data, cheaper to run, and easier to trust. We pick the technique that actually performs best on your problem, not the one that sounds most impressive.

### How do we know the forecast can be trusted?

We validate every model against data it hasn't seen and tell you plainly how accurate it is, and, just as important, where it's weak. A forecast with honest error bars beats a single confident number that hides its uncertainty. You'll know when to lean on it and when to apply judgment.

### What do we own?

The trained models, the code, and the documentation. You can run them, retrain them as new data arrives, and extend them, on your infrastructure, with no per-prediction fee and no lock-in.