  The classification of time-series data is pivotal for streaming data and
comes with many challenges. Although the amount of publicly available datasets
increases rapidly, deep neural models are only exploited in a few areas.
Traditional methods are still used very often compared to deep neural models.
These methods get preferred in safety-critical, financial, or medical fields
because of their interpretable results. However, their performance and
scale-ability are limited, and finding suitable explanations for time-series
classification tasks is challenging due to the concepts hidden in the numerical
time-series data. Visualizing complete time-series results in a cognitive
overload concerning our perception and leads to confusion. Therefore, we
believe that patch-wise processing of the data results in a more interpretable
representation. We propose a novel hybrid approach that utilizes deep neural
networks and traditional machine learning algorithms to introduce an
interpretable and scale-able time-series classification approach. Our method
first performs a fine-grained classification for the patches followed by sample
level classification.
