Smoothing, Baselines, and Naive Forecasts

Install and import#

bash

npm install fintech-algorithms

import { smoothingBaselinesAndNaiveForecasts } from "fintech-algorithms/foundations/financial-time-series-foundations/smoothing-baselines-and-naive-forecasts";

Signature#

smoothingBaselinesAndNaiveForecasts(input)

Runs simple exponential smoothing over the series and reports both the naive last-value forecast and the smoothed one.

Parameters#

Name	Type	Notes
`input`	`D00Input`	Reads `values`, a non-empty list of finite numbers, `timestamps` of the same length, and `alpha`, the weight placed on the newest observation.

Returns#

D00Output

smoothed is the same length as the input and starts at its first value. naiveNext is the last observed level and smoothedNext the last smoothed one.

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 alpha is outside the closed interval from zero to one — throws RangeError

Complexity: time O(n^2), 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#

smoothingBaselinesAndNaiveForecasts(input)

Returns#

object with 2 fields: smoothed, naiveNext

{
  "smoothed": [
    100,
    100.80000000000001,
    100.88000000000001,
    102.12800000000001,
    103.67680000000001,
    104.20608000000001
  ],
  "naiveNext": 105
}