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pith:L5GMCCH7

pith:2026:L5GMCCH7FXUXN75YGNCHGIEP4H

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Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation

Bhanu Suraj Malla, Yuqing Hu

Stationarity transformations improve time series forecasts only 18 percent of the time even when matched to the data.

arxiv:2605.17689 v1 · 2026-05-17 · stat.ME · math.ST · stat.TH

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C1strongest claim

For matched pairs, transforms improve forecasts only 18% of the time. The primary exception is variance stabilization: log and Box-Cox on heteroscedastic data improve accuracy in 60-65% of cases. Mediation analysis confirms that while transforms achieve trend stationarity, this does not translate into lower forecast error; the mechanism is signal attenuation.

C2weakest assumption

The synthetic datasets accurately isolate and represent the non-stationarity types (trend, seasonality, heteroscedasticity) that matter for real forecasting performance, and the consensus ratio from ten statistical tests reliably identifies when a transform is matched to the data.

C3one line summary

Large-scale experiments on synthetic data find stationarity transformations improve forecasts in only 18% of matched cases, with variance stabilization as the main exception and signal attenuation as the mechanism.

References

19 extracted · 19 resolved · 1 Pith anchors

[1] Box, G.E.P .; Jenkins, G.M.Time Series Analysis: Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1970 1970

[2] Hyndman, R.J.; Athanasopoulos, G.Forecasting: Principles and Practice, 2nd ed.; OTexts: Melbourne, Australia, 2018 2018

[3] An analysis of transformations.J

[4] The estimation and application of long memory time series models.J 1983

[5] The M3-Competition: Results, conclusions and implications.Int 2000

Receipt and verification

First computed	2026-05-20T00:04:52.900283Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5f4cc108ff2de976ffb8334473208fe1ea0dba1c170e17704b3bc49367936f7d

Aliases

arxiv: 2605.17689 · arxiv_version: 2605.17689v1 · doi: 10.48550/arxiv.2605.17689 · pith_short_12: L5GMCCH7FXUX · pith_short_16: L5GMCCH7FXUXN75Y · pith_short_8: L5GMCCH7

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/L5GMCCH7FXUXN75YGNCHGIEP4H \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5f4cc108ff2de976ffb8334473208fe1ea0dba1c170e17704b3bc49367936f7d

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-17T23:02:53Z",
    "title_canon_sha256": "9286baf2d5d3faa577d5c89e639d594c088e42beb0b7b2a314361c31b1292cdf"
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