Models & methods
From ARIMA to foundation models: forecasting methods compared
Classical models are interpretable and cheap. Machine learning eats more drivers. Foundation models arrive pretrained and can go zero-shot. None of them win every series — which is why serious stacks keep a tournament, not a favorite.
Updated Aug 13, 2026·10 min read
Always a baseline
Last value. Last week. Last year. A seasonal naive. If you skip this, every other method gets a participation trophy. Baselines are not dummy work. They are the price of admission, and they should run in production as a watchdog — when the champion loses to last week three origins in a row, something is stale.
The methods, side by side
- 01Naive / seasonal naiveHard to beat on stable clocksCheap
- 02Smoothing, ARIMAOne series, a statisticianLow
- 03Gradient boostingLots of drivers, many seriesMedium
- 04Foundation (zero-shot)Short history, related seriesAPI / GPU
- 05Fine-tuned / customWhen the backtest pays for itHighest
Smoothing and ARIMA
Exponential smoothing and ARIMA are still respectable on a single well-behaved series with a person who can read a residual. They are a slow way to lose when you have thousands of SKUs, stockouts, and a promo calendar that looks like a ransom note. Use them as candidates, not as an identity.
“Is ARIMA outdated?” is the wrong question. For one series and a statistician in the chair, no. For a retailer on Tuesday morning, usually yes.
Machine learning
Gradient boosting (and friends) eat lags, calendars, and covariates without requiring the series to sit still. They like panels: many related series, shared features. They will overfit a kitchen sink. They need the same walk-forward honesty as everyone else. Tree models do not get a free pass on leakage.
Foundation models
Pretrained on many series, then used zero-shot or fine-tuned. They help when history is short or when you want a strong first path without a custom loop. They are not magic on a one-off series with a private calendar. They still like drivers you will have at inference. Put them in the tournament. Do not replace the tournament with a blog post about tokens.
How to choose
- Same data, same folds, same horizon for every candidate.
- Include a naive. Include one simple statistical model.
- Promote on the metric ops feels, not the one that flattered the lab.
- Complexity is a cost. Climb a rung only when error moved enough to change a PO.
FAQ
- What is the best forecasting model?
- The one that wins a fair backtest on your series, grain, and horizon. That winner changes when the business changes. Treat 'best' as a temporary job title.
- Is ARIMA outdated?
- For a single well-behaved series with a statistician in the chair, it is still a respectable tool. For thousands of SKUs with promos and stockouts, it is a slow way to lose a tournament.
- When do foundation models help?
- Short history, many related series, or when you want a strong first path without a custom training loop. They are not magic on a one-off series with a unique promo calendar — they still like good drivers.
Keep going
Guide
Zero-shot forecasting
Zero-shot means you get a forecast without training on your series first. A foundation model uses what it learned from many other series, then reads yours. Fine-tuning is the later step, once you have enough of your own data to make it worth the spend.
Guide
Why one model never wins
A model that dominated last spring can lose in November. Series change, drivers change, and luck exists. Keep a challenger on the same folds, promote on evidence, and stay loyal to the score — not to the architecture that won a bake-off in March.
Guide
Time series forecasting, explained
Time series forecasting predicts the next values of something that was recorded in order — sales by day, load by hour, tickets by week — by learning from its own past, and from anything that regularly moves with it.
Use case
Energy load forecasting
Forecast demand by interval, feeder, zone, and customer class with weather-driven uncertainty.
Use case
Revenue forecasting
Maintain a current revenue outlook grounded in bookings, pipeline, usage, renewals, pricing, and seasonality.