Forecast any number with eight forecasting algorithms

The context

Forecasting has always fascinated me.
About five years after my first FinOps talk — a lightning session on forecasting at FinOps X in Austin (it’s still on YouTube) — I realised that even as the FinOps community evolved, forecasting remains one of the hardest and most debated topics.

We still talk about unit economics, budgets, and cost optimisation — but forecasting sits right next to them.
It’s a discipline that cuts across industries: banks, retailers, logistics, and energy companies all forecast. It’s a field with real history, tested methodologies, and mathematical rigour.

And yet, in cloud, we often treat it as guesswork.

I wanted to change that — or at least make it easier to explore.
I don’t claim to know every formula, but I wanted a tool that could run several methods at once, let me compare them, and see which one fits the data and context best.

So I built forecast_costs.py — a simple, composable tool that takes a CSV with dates and numbers, applies multiple forecasting techniques, and shows how they behave.

In spirit, it follows the same pattern as the rest of the FinOps Toolkit: small, local, contextual, and transparent.
The forecasts are not just for cloud spend — they work for any time series, any number, any system where you need to see what might happen next.

And yes, I also asked AI to help.
Sometimes I let it pick which algorithm fits the data best. Sometimes I override it. That’s the balance I like — humans and machines exploring context together.

Why it exists

Forecasting is everywhere — but it’s rarely approachable.
In FinOps, it often feels like a black box: either too simple (“just extrapolate”) or too complex (“we’ll deploy a neural network”).

The goal of forecast_costs.py is to make forecasting simple, plural, and transparent.

You feed it a CSV. It runs multiple methods. You compare the results.

No dashboards. No hidden models. Just clean outputs you can reason about.