01300nas a2200121   4500000000100000008004100001260003600042100002000078700001900098245008700117856006000204520091400264       2021                            d  c09/2021aTârgu Mureș, Romania1 aKamil Książek1 aKrisztian Buza00aA hybrid CNN-SVM approach with dynamic time warping for time series classification  uhttps://mathinfo.ms.sapientia.ro/2021/abstract-book.pdf3 a<p>Recently, convolutional neural networks (CNNs) have been successfully applied in many fields, including time series classication. However, in earlier years an important role played methods based on a dynamic time warping (DTW) distance. Therefore some researchers tried to combine the advantages of CNNs and DTW and create hybrid methods. In this work, we demonstrate a new approach which consists of three main stages. In the initial phase, a conventional CNN is trained. Then, based on weights from the rst convolutional layer of the learned network, DTW distances are calculated. Finally, such extracted features are used for the training of a traditional classier like Support Vector Machine (SVM). The use of SVM instead of another neural network should reduce the whole calculation time and ensure the more explainable model. We present initial results of this method for selected datasets.</p>