Are we overusing neural networks for time-series analysis?
Neural networks are incredible tools.
But in many real-world time-series problems, they’re simply overkill.
If your signal is driven by a handful of rhythms, trends, decays, or saturations, training a model with thousands (or millions) of parameters can be like using a jet engine to stir tea. You’ll get motion - but also compute cost, power draw, latency, and deployment pain.
That’s why I built and open-sourced Time Series Formula Finder which takes a different approach:
• Find the simplest mathematical forms that explain the data
• Decompose signals into layers of structure
• Produce human-readable equations, not black boxes
Instead of “trust this model,” the output is:
“Here’s the equation that explains your signal.”
Why formulas often beat NNs in practice
1⃣ Edge deployment becomes trivial
A formula runs in a few operations per sample. No GPU. No heavy runtime. Ideal for microcontrollers, industrial devices, and battery-powered sensors.
2⃣ Interpretability is the feature
In engineering and operational domains, explanation matters. A formula exposes frequency, decay rates, drift, and saturation directly.
3⃣ Lower lifetime cost
Neural networks invite retraining cycles, monitoring pipelines, drift detection, and version churn. A good equation often stays valid far longer.
4⃣ Debugging is possible
If performance drops, you can ask which term stopped matching reality. With NNs, the answer is usually far less clear.
What the tool actually does is search for promising partial forms: expressions that explain part of a signal well, subtract them, then analyze the residual.
This produces a layered explanation rather than a monolithic model.
Neural networks are incredible tools.
But in many real-world time-series problems, they’re simply overkill.
If your signal is driven by a handful of rhythms, trends, decays, or saturations, training a model with thousands (or millions) of parameters can be like using a jet engine to stir tea. You’ll get motion - but also compute cost, power draw, latency, and deployment pain.
That’s why I built and open-sourced Time Series Formula Finder which takes a different approach: • Find the simplest mathematical forms that explain the data • Decompose signals into layers of structure • Produce human-readable equations, not black boxes
Instead of “trust this model,” the output is: “Here’s the equation that explains your signal.”
Why formulas often beat NNs in practice 1⃣ Edge deployment becomes trivial
A formula runs in a few operations per sample. No GPU. No heavy runtime. Ideal for microcontrollers, industrial devices, and battery-powered sensors.
2⃣ Interpretability is the feature In engineering and operational domains, explanation matters. A formula exposes frequency, decay rates, drift, and saturation directly.
3⃣ Lower lifetime cost Neural networks invite retraining cycles, monitoring pipelines, drift detection, and version churn. A good equation often stays valid far longer.
4⃣ Debugging is possible If performance drops, you can ask which term stopped matching reality. With NNs, the answer is usually far less clear.
What the tool actually does is search for promising partial forms: expressions that explain part of a signal well, subtract them, then analyze the residual.
This produces a layered explanation rather than a monolithic model.
All feedback appreciated!