Lags, Leads, and Temporal Dependence

Install and import#

bash

npm install fintech-algorithms

import { lagsLeadsAndTemporalDependence } from "fintech-algorithms/foundations/financial-time-series-foundations/lags-leads-and-temporal-dependence";

Signature#

lagsLeadsAndTemporalDependence(input)

Pairs every observation with the value lag positions behind it, and exposes the same series shifted forward as the lead.

Parameters#

Name	Type	Notes
`input`	`D00Input`	Reads `values`, a non-empty list of finite numbers, `timestamps` of the same length, and `lag`, the whole number of positions to shift by.

Returns#

D00Output

laggedPairs holds one object per shiftable position with current and lagged, leadValues is the series with its first lag entries dropped, and lag is echoed back.

Errors#

When values is absent, empty, or holds a non-finite number — throws RangeError
When timestamps and values have different lengths — throws RangeError
When the series holds fewer than two observations — throws RangeError
When lag is not an integer between one and the observation count minus one — throws RangeError

Complexity: time O(n), space O(n).

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

input

{
  "timestamps": [
    "2025-01-01T00:00:00Z",
    "2025-01-02T00:00:00Z",
    "2025-01-03T00:00:00Z",
    "2025-01-04T00:00:00Z",
    "2025-01-05T00:00:00Z",
    "2025-01-06T00:00:00Z"
  ],
  "values": [100, 102, 101, 104, 106, 105],
  "lag": 1,
  "window": 3,
  "resampleSize": 2,
  "period": 3,
  "stationarityTolerance": 3,
  "alpha": 0.4,
  "splitIndex": 4
}

Call#

lagsLeadsAndTemporalDependence(input)

Returns#

object with 2 fields: laggedPairs, leadValues

{
  "laggedPairs": [
    {
      "current": 102,
      "lagged": 100
    },
    {
      "current": 101,
      "lagged": 102
    },
    {
      "current": 104,
      "lagged": 101
    }
  ],
  "leadValues": [102, 101, 104, 106, 105]
}