An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series
Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited app...
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MDPI AG
2025-02-01
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| author | Colin O. Quinn Ronald H. Brown George F. Corliss Richard J. Povinelli |
| author_facet | Colin O. Quinn Ronald H. Brown George F. Corliss Richard J. Povinelli |
| author_sort | Colin O. Quinn |
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| description | Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods. |
| format | Article |
| id | doaj-art-222bdc52bfde4e68b1c8d9cddb1006af |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-222bdc52bfde4e68b1c8d9cddb1006af2025-08-20T02:48:10ZengMDPI AGSensors1424-82202025-02-0125389510.3390/s25030895An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time SeriesColin O. Quinn0Ronald H. Brown1George F. Corliss2Richard J. Povinelli3Department of Computer Science, Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI 53233, USAMarquette Energy Analytics LLC, 313 North Plankinton Avenue, Suite 206, Milwaukee, WI 53203, USADepartment of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233, USADepartment of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233, USAAccurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods.https://www.mdpi.com/1424-8220/25/3/895time series disaggregationgas consumption disaggregationnonuniform samplingconstrained redistributiontime series forecastingmulti-source |
| spellingShingle | Colin O. Quinn Ronald H. Brown George F. Corliss Richard J. Povinelli An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series Sensors time series disaggregation gas consumption disaggregation nonuniform sampling constrained redistribution time series forecasting multi-source |
| title | An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series |
| title_full | An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series |
| title_fullStr | An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series |
| title_full_unstemmed | An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series |
| title_short | An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series |
| title_sort | iterative shifting disaggregation algorithm for multi source irregularly sampled and overlapped time series |
| topic | time series disaggregation gas consumption disaggregation nonuniform sampling constrained redistribution time series forecasting multi-source |
| url | https://www.mdpi.com/1424-8220/25/3/895 |
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