A signal is an observable change that carries information about a source, state, event or condition.
A warning light, price movement, exam result, sensor reading, facial expression, search trend, radio transmission, heartbeat trace and error log can all function as signals. Their meaning depends on source, channel, timing, strength, noise, context and reliability.
Quick answer: how should signals be categorised?
- Source: where does the signal originate?
- Channel: physical, visual, auditory, digital, statistical, social?
- Strength: how large is the observed change?
- Timing: when does it occur and how long does it persist?
- Frequency: one-off, periodic, continuous, burst?
- Reliability: how often does it correspond to the underlying condition?
- Noise: what irrelevant variation interferes?
- Direction: increasing, decreasing, stable, oscillating?
- Meaning: what state or event does it indicate?
- Actionability: should the signal trigger observation, review or intervention?
This article applies How to Categorise Anything to signals as information-bearing observations.
1. Separate signal from source
A sensor is a source; the reading it produces is a signal.
2. Separate signal from meaning
A flashing light is an observation; “machine overheating” is an interpretation of what the light signifies.
3. Signals can be natural or designed
Smoke can signal fire naturally; an alarm is engineered to communicate a warning.
4. Signals can be direct or proxy
Direct measurements observe the variable itself; proxy signals infer a hidden state through correlated behaviour.
5. Channel matters
Optical, acoustic, electrical, textual, statistical and behavioural channels have different failure modes.
6. Signal strength is not importance
A weak but specific signal can matter more than a large but noisy one.
7. Baseline defines deviation
Many signals only become meaningful relative to a normal or expected range.
8. Thresholds create alerts
A continuous signal can trigger a category change once it crosses a governed threshold.
9. Noise obscures signal
Noise can arise from measurement error, irrelevant variation, interference or random fluctuation.
10. Signal-to-noise ratio is contextual
A useful signal is strong enough relative to background variation for the intended task.
11. False positives are signal failures
The signal indicates a condition that is not actually present.
12. False negatives are missed signals
The condition is present but the expected signal is absent or too weak.
13. Sensitivity and specificity differ
Some signals detect most true cases but generate many false alarms; others are highly specific but miss weak cases.
14. Timing can define signal class
Leading, coincident and lagging signals relate to the event or state at different times.
15. Leading signals support anticipation
They change before the outcome of interest and can support early warning.
16. Lagging signals confirm aftermath
They may be reliable evidence that a change occurred even when they arrive too late for prevention.
17. Frequency matters
Continuous, periodic and event-triggered signals require different monitoring architectures.
18. Duration matters
A brief spike and a persistent shift may deserve different classifications even at the same magnitude.
19. Trend is not event
A gradual rise across months differs from one sudden excursion.
20. Direction can carry meaning
Increasing, decreasing, stable and oscillating patterns can represent different system states.
21. Context changes interpretation
The same signal value can be normal in one environment and abnormal in another.
22. Multi-signal fusion improves confidence
Independent signals from different channels can corroborate one another.
23. Correlated signals can double count
Several indicators derived from one underlying source may look like independent confirmation when they are not.
24. Signal provenance matters
Record device, source, transformation, calibration and version where these affect interpretation.
25. Missing signal is ambiguous
Absence may mean normal state, sensor failure, communication loss or missing data.
26. Confidence should accompany inferred meaning
A signal may be observed with certainty while its interpretation remains uncertain.
27. Signals can trigger tasks
An alert can create inspection, escalation or recovery work.
28. Signals can trigger decisions
Threshold crossings may route a case into a decision rule or human review.
29. Actionability needs cost awareness
A weak signal may not justify expensive action even if it deserves closer observation.
30. High-stakes signals need validation
Where false alarms or misses are costly, use corroboration, calibration and review.
31. AI can discover latent signals
Models can identify patterns too complex for simple thresholds, but learned signals require testing for stability and bias.
32. Learned signals can drift
Relationships between features and outcomes can weaken as behaviour or environment changes.
33. Signal dictionaries need controlled meaning
Names, units, direction, thresholds and interpretation rules should be explicit.
34. Derived indicators should preserve lineage
A composite score should link to the underlying measurements and formula.
35. Alerts are classified signals
An alert is usually a signal plus threshold, interpretation and routing policy.
36. Signal taxonomies drift
New sensors, behaviours and channels create new indicators while old signals may lose meaning.
37. A practical signal record
- signal ID;
- source;
- channel;
- measurement or observation;
- unit;
- baseline;
- strength;
- direction;
- timing;
- frequency;
- noise;
- reliability;
- interpreted meaning;
- confidence;
- thresholds;
- action route;
- version.
38. Signal categories should improve detection
A useful classification changes how the signal is filtered, combined, interpreted or acted upon.
39. Signal is not truth
Signals are evidence-bearing observations whose meaning remains dependent on model and context.
40. The deeper idea
A signal is the interface between hidden state and observable change.
To categorise a signal well is to know where it came from, how strongly it differs from noise, what state it may indicate and what action that interpretation justifies.
Final answer
Categorise signals by source, channel, strength, timing, frequency, reliability, noise, direction, meaning and actionability. Separate signal from source and interpretation, preserve baseline and provenance, and use corroboration when consequential decisions depend on the signal.