Time Series Classification with Deep Learning


First of all it's important to underline why this problem is so important today, and therefore why it is very interesting to understand the role and the potential of Deep Learning in this sector. During the last years, Time Series Classification has become one of the most challenging problems in Data Science. This has happened because any classification problem that uses data keeping in consideration some notion of sorting, can be treated as a Time Series Classification problem. Time series are present in many real-world applications ranging from health care, human activity recognition, cyber-security, finance, marketing, automated disease detection, anomaly detection, etc. As the availability of temporal data has increased significantly in the last years, many areas are becoming strongly interested in applications based on time series, and then many new algorithms have been proposed. All these algorithms, apart from those based on deep learning, require some kind of feature engineering as a separate task before the classification is performed, and this can imply the loss of some information and the increase of the development time. On the contrary, deep learning models already incorporate this kind of feature engineering internally, optimizing it and eliminating the need to do it manually.

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