PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification

Classification of time-series data is pivotal for a wide range of applications and comes with many challenges. Although the amount of publicly available datasets increases rapidly, deep neural models are only partially exploited in contrast to the traditional methods. These methods get preferred in safety-critical, financial, or medical fields because of their interpretable results. However, their performance and scalability are limited, and finding suitable explanations for time-series classification tasks is challenging due to the intrinsic nature of concealed concepts in the time-series data. Visual analysis of complete time-series comes with an extensive cognitive overload, as it is difficult to perceive and leads to confusion. Therefore, we believe that patch-wise processing of the data results in a more interpretable representation. To bridge this gap, and to reduce the cognitive overload for interpretation of time series data, we propose a novel hybrid approach that utilizes deep neural networks and traditional machine learning algorithms for an interpretable and scale-able time-series classification approach. Both quantitively and qualitatively PatchX shows superiority to its counterparts with an edge of interoperability.