A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification
<|reference_start|>A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification: Rainfall estimation through the analysis of its impact on electromagnetic waves has sparked increasing interest in the research community. Recent studies have delved into its effects on cellular network performance, demonstrating the potential to forecast rainfall levels based on electromagnetic wave attenuation during precipitations. This paper aims to solve the problem of identifying the nature of specific weather phenomena from the received signal level (RSL) in 4G/LTE mobile terminals. Specifically, utilizing time-series data representing RSL, we propose a novel approach to encode time series as images and model the task as an image classification problem, which we finally address using convolutional neural networks (CNNs). The main benefit of the abovementioned procedure is the opportunity to utilize various data augmentation techniques simultaneously. This encompasses applying traditional approaches, such as moving averages, to the time series and enhancing the generated images. We have investigated various image data augmentation methods to identify the most effective combination for this scenario. In the upcoming sections, we will introduce the task of rainfall estimation and conduct a comprehensive analysis of the dataset used. Subsequently, we will formally propose a new approach for converting time series into images. To conclude, the paper's final section will present and discuss the experiments conducted, providing the reader with a brief yet comprehensive overview of the results.<|reference_end|>
@article{giannetti2024a,
    title={A Novel Time Series-to-Image Encoding Approach for Weather Phenomena
  Classification},
    author={Christian Giannetti},
    journal={arXiv preprint arXiv:2406.05096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05096},
    primaryClass={cs.CV}
}