Can we protect time series data while maintaining accurate forecasts?

Cameron D. Bale, Matthew J. Schneider and Jinwook Lee

International Journal of Forecasting, 2026, vol. 42, issue 2, 297-314

Abstract: We evaluate the usefulness of protected time series by exploring how privacy protection affects forecast accuracy. Using both simulated and real-world time series data sets, we test various privacy methods, including a proposed swapping-based method (k-nTS+) designed to maintain time series features, a differentially private method, and an approach based on sharing model weights trained on unprotected data. Based on forecasts from both simple and machine learning models, we find that none of the privacy methods can consistently maintain forecast accuracy at an acceptable level of privacy. We also show that sharing model weights enables accurate forecasts, but accurate forecasts can be used to uncover the identities of protected time series. To overcome these problems, we transform continuous time series into bounded rates to increase the similarities of features, values, and forecasts across time series. This enables our proposed method to produce protected data with a reduction in average forecast accuracy of just 6%. Overall, we acknowledge that except under certain conditions, generating time series with acceptable privacy levels is incompatible with the goal of obtaining accurate forecasts.

Keywords: Data privacy; Time series features; Machine learning; Forecast accuracy; Differential privacy (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207025000019
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:42:y:2026:i:2:p:297-314

DOI: 10.1016/j.ijforecast.2025.01.001

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().