# 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.