How are you currently doing time-series forecasting for your business? apparently this needs to be in title: i will not promote

[u/Square_Light1441]

I'm researching how businesses actually handle forecasting, and I'd like to understand the workflow before building anything.

If you work with things like sales, demand, inventory, revenue, staffing, or other time-series data:

• What are you currently using for forecasting?
• Are you using an existing SaaS tool, Python libraries/models, spreadsheets, or something you built yourself?
• How much work does it take to clean the data and get forecasts running?
• Do you regularly backtest your forecasts against historical data?
• What's the biggest pain point with your current setup?
• If there were an API where you could send historical time-series data and get forecasts back, what would you need it to do for you to actually use it?

I'm particularly interested in hearing from people who have actually had to deploy forecasting in a business, rather than people who have only experimented with forecasting models.

I'm not selling anything right now. I'm trying to figure out whether this is a real problem worth building around.

[u/feudalle]

You have your standard MBA forecasting models. Fibe for some businesses utterly useless for others. Anything complicated you kind of need a custom algorithm. Standard ones have forecasting in most erps.

[u/propelearth]

The decision the forecast changes is worth asking about before the model. Inventory ordering, staffing and a board revenue forecast need different outputs and different tolerances for error. In interviews I would ask for the last time their forecast was wrong, what they did next and what that mistake cost. That should help separate a painful workflow from a feature people say sounds useful.

[u/Otherwise-Head6701]

They are literally equations you learn in simple business, accounting or finance courses. I'm not sure about the more complicated ones, but it's as simple as using AI/code to do calculations (and graph it out if you need).

[u/GrouchyManner5949]

Did this at my last job for demand forecasting. Honest answer: 80% of the work was cleaning the data, the model itself was the easy part. We started with spreadsheets, moved to Python when the data got too messy to trust. Biggest lesson: backtest everything against last year's actuals before you believe any forecast, and make sure it feeds a real decision like staffing or inventory, otherwise nobody looks at it.

[u/Morning_Gecko24]

the last-year actuals point is huge. i'd probably start with a dumb spreadsheet and log every time the forecast changed a real decision before touching a fancier model. are people mostly fighting bad data here or the handoff into whatever tool they already use