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Found 5 papers, 4 papers with code
Multivariate Time Series Early Classification Across Channel and Time Dimensions
Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent.
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions
We also show that if we keep the transformation method constant, there is a statistically significant difference in accuracy results when applying it across different dimensions, with accuracy differences ranging from 0. 23 to 47. 79 percentage points.
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS
We show that we achieve speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification
Specifically, utilizing a wavelet scattering transformation of the time series and distributed feature selection, we manage to create a solution which employs just 2, 5% of the ROCKET features, while achieving accuracy comparable to recent deep learning solutions.
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks
We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models.
