Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting
Time series decomposition enables the extraction of meaningful components by filtering out irrelevant or noisy frequencies. This study investigates the use of four transformation techniques—three wavelet-based (Haar, Symlets, Daubechies) and one Fourier-based (Discrete Fourier Transform)—to reconst...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/139016 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850277115323219968 |
|---|---|
| author | Victor Solomon Junzhi Wen Rafal Angryk Manya Rampuria Omkar Rayala Abdul Afrid |
| author_facet | Victor Solomon Junzhi Wen Rafal Angryk Manya Rampuria Omkar Rayala Abdul Afrid |
| author_sort | Victor Solomon |
| collection | DOAJ |
| description |
Time series decomposition enables the extraction of
meaningful components by filtering out irrelevant or
noisy frequencies. This study investigates the use of
four transformation techniques—three wavelet-based
(Haar, Symlets, Daubechies) and one Fourier-based
(Discrete Fourier Transform)—to reconstruct multivari
ate time series data for solar flare prediction. The recon
structed data is used to train and evaluate two classifiers:
Time Series Forest (TSF) for time series inputs, and
Random Forest (RF) for transformed non-time-series
representations.
We describe the data preparation pipeline, model train
ing, and comparative performance analysis of TSF
and RF. Additionally, we assess the effectiveness of
data reduction by evaluating model performance on re
duced datasets. Results show that TSF models consis
tently outperform RF models, and that reduced datasets
yield competitive predictive performance. These find
ings suggest the potential for computational efficiency
in near-real-time flare prediction without significant
loss in accuracy. Evaluation metrics include the True
Skill Statistic (TSS) and Heidke Skill Score (HSS2).
|
| format | Article |
| id | doaj-art-c76b3bcae41d40aaaa0d632bc0f38321 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-c76b3bcae41d40aaaa0d632bc0f383212025-08-20T01:49:59ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.139016Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare ForecastingVictor Solomon0Junzhi WenRafal AngrykManya RampuriaOmkar RayalaAbdul AfridGeorgia State University Time series decomposition enables the extraction of meaningful components by filtering out irrelevant or noisy frequencies. This study investigates the use of four transformation techniques—three wavelet-based (Haar, Symlets, Daubechies) and one Fourier-based (Discrete Fourier Transform)—to reconstruct multivari ate time series data for solar flare prediction. The recon structed data is used to train and evaluate two classifiers: Time Series Forest (TSF) for time series inputs, and Random Forest (RF) for transformed non-time-series representations. We describe the data preparation pipeline, model train ing, and comparative performance analysis of TSF and RF. Additionally, we assess the effectiveness of data reduction by evaluating model performance on re duced datasets. Results show that TSF models consis tently outperform RF models, and that reduced datasets yield competitive predictive performance. These find ings suggest the potential for computational efficiency in near-real-time flare prediction without significant loss in accuracy. Evaluation metrics include the True Skill Statistic (TSS) and Heidke Skill Score (HSS2). https://journals.flvc.org/FLAIRS/article/view/139016solar flare predictiontime series decompositionwavelet transformFourier transformmulti-variate |
| spellingShingle | Victor Solomon Junzhi Wen Rafal Angryk Manya Rampuria Omkar Rayala Abdul Afrid Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting Proceedings of the International Florida Artificial Intelligence Research Society Conference solar flare prediction time series decomposition wavelet transform Fourier transform multi-variate |
| title | Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting |
| title_full | Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting |
| title_fullStr | Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting |
| title_full_unstemmed | Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting |
| title_short | Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting |
| title_sort | time series decomposition using wavelet and fourier transforms for enhanced solar flare forecasting |
| topic | solar flare prediction time series decomposition wavelet transform Fourier transform multi-variate |
| url | https://journals.flvc.org/FLAIRS/article/view/139016 |
| work_keys_str_mv | AT victorsolomon timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting AT junzhiwen timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting AT rafalangryk timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting AT manyarampuria timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting AT omkarrayala timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting AT abdulafrid timeseriesdecompositionusingwaveletandfouriertransformsforenhancedsolarflareforecasting |