Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data
In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4759 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405936485007360 |
|---|---|
| author | David Orlando Salazar Torres Diyar Altinses Andreas Schwung |
| author_facet | David Orlando Salazar Torres Diyar Altinses Andreas Schwung |
| author_sort | David Orlando Salazar Torres |
| collection | DOAJ |
| description | In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. |
| format | Article |
| id | doaj-art-e48b670f4e2e4a7a8821730c9b38ab99 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e48b670f4e2e4a7a8821730c9b38ab992025-08-20T03:36:33ZengMDPI AGSensors1424-82202025-08-012515475910.3390/s25154759Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series DataDavid Orlando Salazar Torres0Diyar Altinses1Andreas Schwung2Department of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyDepartment of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyDepartment of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences, 59494 Soest, GermanyIn time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks.https://www.mdpi.com/1424-8220/25/15/4759Bag of Functions frameworkmulti-resolution signalsresamplingtime-invariant methodstime series decompositionvariable sampling rates |
| spellingShingle | David Orlando Salazar Torres Diyar Altinses Andreas Schwung Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data Sensors Bag of Functions framework multi-resolution signals resampling time-invariant methods time series decomposition variable sampling rates |
| title | Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data |
| title_full | Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data |
| title_fullStr | Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data |
| title_full_unstemmed | Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data |
| title_short | Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data |
| title_sort | resampling multi resolution signals using the bag of functions framework addressing variable sampling rates in time series data |
| topic | Bag of Functions framework multi-resolution signals resampling time-invariant methods time series decomposition variable sampling rates |
| url | https://www.mdpi.com/1424-8220/25/15/4759 |
| work_keys_str_mv | AT davidorlandosalazartorres resamplingmultiresolutionsignalsusingthebagoffunctionsframeworkaddressingvariablesamplingratesintimeseriesdata AT diyaraltinses resamplingmultiresolutionsignalsusingthebagoffunctionsframeworkaddressingvariablesamplingratesintimeseriesdata AT andreasschwung resamplingmultiresolutionsignalsusingthebagoffunctionsframeworkaddressingvariablesamplingratesintimeseriesdata |