Time series forecasting models play a crucial role as decision support tools in various real-world domains. Among them, stock market represents a notably complex domain, characterized by a rapidly evolving temporal nature and a multitude of factors influencing stock prices. While many machine learning-based approaches...
Pi Chen, Roberto Corizzo· International Journal of Dat...· 0 citations
Time series anomaly detection (TSAD) underpins critical applications in manufacturing, healthcare, finance, and cloud computing. Recently, time series to image (TS2I) transformations have emerged as a promising approach that enables leveraging pretrained vision models for time series analysis. However, the majority of...
Mateusz Smendowski, Kamil Faber, Piotr Nawrocki et al.· Machine-mediated learning· 0 citations
While anomaly detection is essential for cloud computing and predictive maintenance, approaches that bridge continual learning with environmental sustainability remain largely unexplored. In production environments, evolving data distributions cause performance degradation of machine learning models, and naive adaptati...
Mateusz Smendowski, Roberto Corizzo, Nathalie Japkowicz et al.· Journal of Grid Computing· 0 citations
PRISM is introduced, a plug-and-play meta-workflow enabling systematic construction and evaluation of image-based representations for multivariate TSAD and channelization is identified - how the channel dimension of multi-channel images is constructed - as a critical and previously understudied design dimension.
Mateusz Smendowski, Kamil Faber, Piotr Nawrocki et al.· 0 citations
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