Forecasting & predictive analytics
Orders, stock, hiring, cash flow: you have to decide today for the months ahead, often on gut feeling. A forecast with its margin of error makes those decisions safer.
Try it: forecast the next few months of sales
Seasonal sample data. Pick a horizon: the shaded area shows the likely range, which widens the further ahead you look.
- Actual sales
- Forecast
- Likely range (80%)
View the forecast as a table
Method shown: trend + seasonality, validated on the last 12 known months. On your data, several methods are compared before one is chosen.
In practice
- Sales, demand or cash-flow forecasts
- Seasonality and trends taken into account
- A range of uncertainty, not just a single number
- Early warning of risks (customers dropping off, stock-outs)
- Built into your dashboard and updated automatically
Tools
- Python
- Excel
- Power BI
- SQL
How the project runs
- 1
History
We gather and check at least 2 years of data where possible.
- 2
Model
I test several methods and keep the one that performs best on your past data.
- 3
Validation
We compare the forecast with what actually happened.
- 4
Integration
The forecast updates itself in your tools.
Your questions
How much history do I need?
Ideally 2 years or more to capture seasonality. With less, we stick to simple trends and a wider margin of error.
Is this artificial intelligence?
Sometimes, but not always. A simple, well-explained statistical method often beats a complex model. I pick whatever proves most reliable on your data.
Other services
Clear Power BI or Excel dashboards that refresh themselves and that your team actually uses. Process automation
Repetitive tasks (reports, reminders, data entry) run on their own with n8n, Power Automate or Make. Data analysis & cleaning
Clean, deduplicated, consistent data, then analysed to find what really drives your numbers. Express data audit
A quick review of your data and reporting, with a prioritised action plan.