Deep learning models have been shown to be a powerful solution for Time Series
Classification (TSC). State-of-the-art architectures, while conducting promising results on the …

Deep learning models have been shown to be a powerful solution for Time Series
Classification (TSC). State-of-the-art architectures, while producing promising results on the …

Increasing privacy concerns have led to decentralized and federated machine learning
techniques that allow individual clients to consult and train models collaboratively without …

Conventional time series classification approaches based on bags of patterns or shapelets
face significant challenges in dealing with a vast amount of feature candidates from high …

Complex intelligent systems such as for tackling the COVID-19 pandemic involve multiple
multivariate time series (MTSs), where both target variables (such as COVID-19 infected …

Subspace preserving quantum circuits are a class of quantum algorithms that, relying on
some symmetries in the computation, can offer theoretical guarantees for their training …

Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately
defines global warming and climate change, despite the fact that it has effects on different …

Conventional time series classification approaches based on bags of patterns or shapelets
face significant challenges in dealing with a vast amount of feature candidates from high …

This study compares the performance of convolutional neural networks (CNNs) and deep
neural networks (DNNs) for multivariate time series data. We analyse not only final loss …

Abstract Analysis of time series data for classification or prediction tasks is very useful in
various applications such as healthcare, climate studies and finance. As big data resources …