Weak Signal Scanning Horizon Classifier

Assign weak signals to near, medium, or far horizons using caller-supplied year boundaries.

Description

Weak-signal horizons locate anticipated developments relative to explicit near and medium endpoints.

Weak-signal horizons locate anticipated developments relative to explicit near and medium endpoints. Horizon assignment does not establish signal credibility.1

The returned values include Horizon assignments, Horizon counts. The How section below explains the calculation, input rules and interpretation.

When to use Weak Signal Scanning Horizon Classifier

Use Weak Signal Scanning Horizon Classifier when you need to evaluate the supplied records under the calculation and evidence rules below. No signal discovery or universal horizon durations are supplied.

Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.

How to use Weak Signal Scanning Horizon Classifier and understand its results

  1. Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
  2. Enter mediumHorizonEndYears, nearHorizonEndYears, signalHorizonYears, signalIds using the field table and calculation rules, then run the tool.
  3. Read assignments, horizonCounts. The explanation below defines the units, signs, ordering and any flags or placeholder values.
  4. If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
Input fieldMeaningValue type; see rules belowRequirement
mediumHorizonEndYearsMedium-horizon endnumber; minimum: 0Required
nearHorizonEndYearsNear-horizon endnumber; minimum: 0Required
signalHorizonYearsSignal horizon yearslist of numberRequired
signalIdsSignal IDslist of stringRequired

Require nonnegative finite endpoints with near<medium and aligned IDs/years; empty lists are allowed. Trim nonblank IDs, validate nonnegative finite years, preserve order and return assignments and counts in H1/H2/H3 order.

H1 if year≤nearEnd; H2 if year≤mediumEnd; otherwise H3

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output fieldMeaningValue type
assignmentsHorizon assignmentslist of string
horizonCountsHorizon countslist of number
json
{
  "input": {
    "mediumHorizonEndYears": 10,
    "nearHorizonEndYears": 3,
    "signalHorizonYears": [
      1,
      5,
      12
    ],
    "signalIds": [
      "Automation",
      "Grid",
      "Fusion"
    ]
  },
  "output": {
    "assignments": [
      "Automation: H1",
      "Grid: H2",
      "Fusion: H3"
    ],
    "horizonCounts": [
      1,
      1,
      1
    ]
  }
}

Limitations and assumptions

No signal discovery or universal horizon durations are supplied.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Core numeric and string lists are limited to 100,000 entries; text entries are subject to the general large-text limit. Follow any smaller calculation limits above. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Three Horizons Framework Transition Map Lookup: Three horizon labels can organize an entered year series. This implementation uses two explicit start boundaries. For a complementary task, use Horizon Scanning Team Cadence Report Template: A horizon-scanning meeting needs areas, horizons and a review cadence. A template can organize that work before any signals are collected.

References

  1. Weak Signal Scanning Horizon Classifier — tool.io

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