Dead Reckoning

Opening Scene

Long before satellite positioning, navigators relied on dead reckoning: take the last known position, add the estimated speed and heading over the elapsed time, and call that the new position. No fancy instruments, no complex modeling — just the simplest possible extrapolation from the last known point. It’s crude, but it’s also a genuinely strong baseline, and any modern navigation system that can’t beat plain dead reckoning isn’t actually earning its complexity.

In Plain English

A naive forecast predicts that the future will simply look like the most recent observed value (or a simple, recent average). It’s the forecasting equivalent of dead reckoning — deliberately simple, computationally free, and a mandatory baseline every more sophisticated method has to genuinely outperform to justify its own added complexity.

The Old Way

Before formal statistical baselines existed, this exact instinct governed practical forecasting everywhere:

A shopkeeper assuming tomorrow’s demand will resemble today’s, absent any better information.
A weather forecaster’s oldest trick: assume tomorrow’s weather will resemble today’s, a surprisingly strong baseline for short horizons.
A financial analyst’s simplest model: assume tomorrow’s price will resemble today’s, closely related to the efficient-market hypothesis in economics.

In each domain, “assume no change” turned out to be a genuinely hard baseline to beat, not a trivial strawman.

What’s Changing (and Why AI Is the Reason)

Formal forecasting practice now treats naive forecasts as a mandatory benchmark, not an afterthought — any sophisticated model that can’t clear this bar is adding complexity without adding real value.
Naive and seasonal-naive baselines (which repeat the value from the same point in the previous cycle) have become standard, cheap first checks before investing in more complex methods, covered starting with Article 5.
As forecasting has scaled to thousands of sequences at once, covered in Article 19, naive baselines have become genuinely useful as an automated triage tool — flagging which sequences actually need sophisticated modeling and which don’t.

The Metaphor, Fully Extended

The Voyage	Naive Forecasting Concept
Dead reckoning from the last known position	A naive forecast from the most recent observed value
A crude method that’s still surprisingly reliable short-term	A naive baseline that’s genuinely hard to beat
Any modern instrument judged against basic dead reckoning	Any sophisticated model judged against the naive baseline
Triage: which routes need careful navigation, which don’t	Triage: which sequences need sophisticated modeling, which don’t

For Beginners: What to Actually Do

Always compute a naive forecast first, before building anything more sophisticated — it takes minutes and sets a genuine bar.
Learn the seasonal-naive variant for sequences with clear cyclical patterns; it’s often a stronger baseline than plain naive.
Treat “beats the naive baseline” as the actual minimum bar for a model to be worth its added complexity, not a nice-to-have.

For Practitioners and Leaders: The Deeper Layer

Require every forecasting project to report performance against a naive baseline, as a matter of standard practice, not just as an interesting comparison.
Use naive-baseline performance as a cheap, automated triage signal across large sets of sequences, prioritizing modeling effort where it’s genuinely needed.
Be skeptical of any sophisticated model presented without an explicit naive-baseline comparison — its real value can’t be assessed without one.

Quick Recap

A naive forecast simply extrapolates the most recent value (or a seasonal analog) forward.
It’s a deliberately simple, computationally free method that’s still surprisingly hard to beat.
Beating the naive baseline is the actual minimum bar for any more sophisticated forecasting method.
Naive baselines are useful triage tools when forecasting many sequences at scale.

Where This Fits in the Series

Article 3 covered the simplest possible forecast and why it’s a genuine benchmark. Article 4 starts building the vocabulary for describing a sequence’s real structure, beginning with trend.