📈Know When Not to Use ML for Time-Series Forecasting
Stop reaching for LSTMs on tiny series — enforce the baseline ladder (naive, seasonal-naive, ARIMA, then ML), backtest each one properly on real data, and write the decision criteria your team will use to escalate to ML only when the simple model is genuinely beaten.
Phase 1Why the Simple Forecast Usually Wins
Why naive forecasts beat fancy models so often
Yesterday's value is the model to beat
6 minYesterday's value is the model to beat
There is no universally best forecasting model
6 minThere is no universally best forecasting model
200 points is not enough for a transformer
6 min200 points is not enough for a transformer
Pure trend is not what ML is for
5 minPure trend is not what ML is for
Phase 2Run the Baseline Ladder on One Real Series
Backtest naive, seasonal-naive, ARIMA, and an ML model
Choose the series you'll benchmark for the rest of this path
5 minChoose the series you'll benchmark for the rest of this path
Score the two zero-parameter models first
8 minScore the two zero-parameter models first
Add ARIMA — the strongest classical option
10 minAdd ARIMA — the strongest classical option
Now train the ML model — and don't tilt the playing field
12 minNow train the ML model — and don't tilt the playing field
Read your scoreboard like an engineer, not a fan
8 minRead your scoreboard like an engineer, not a fan
Phase 3When Classical Methods Beat ML by Design
When short series and pure trend favor classical methods
Why ML can't escape overfit on short series
8 minWhy ML can't escape overfit on short series
When the signal-to-noise ratio is too low for any model
7 minWhen the signal-to-noise ratio is too low for any model
ML's win must be big enough to pay for itself
8 minML's win must be big enough to pay for itself
The four situations where ML really is the right call
8 minThe four situations where ML really is the right call
Phase 4Write Your Team's ML-Escalation Criteria
Write your team's ML-escalation decision criteria
Write your team's ML-escalation criteria
20 minWrite your team's ML-escalation criteria
Frequently asked questions
- When should I use ML instead of ARIMA for time-series forecasting?
- This is covered in the “Know When Not to Use ML for Time-Series Forecasting” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Why does a naive forecast often beat a neural network on small series?
- This is covered in the “Know When Not to Use ML for Time-Series Forecasting” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What is the no-free-lunch theorem and how does it apply to forecasting?
- This is covered in the “Know When Not to Use ML for Time-Series Forecasting” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do I properly backtest a forecasting model?
- This is covered in the “Know When Not to Use ML for Time-Series Forecasting” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the minimum series length before ML makes sense for forecasting?
- This is covered in the “Know When Not to Use ML for Time-Series Forecasting” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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