Introduction to 1D Convolutional Neural Networks in Keras for Time Sequences

A CNN works well for identifying simple patterns within your data which will then be used to form more complex patterns within higher layers. A 1D CNN is very effective when you expect to derive interesting features from shorter (fixed-length) segments of the overall data set and where the location of the feature within the segment is not of high relevance. This applies well to the analysis of time sequences of sensor data (such as gyroscope or accelerometer data). It also applies to the analysis of any kind of signal data over a fixed-length period (such as audio signals). Another application is NLP (although here LSTM networks are more promising since the proximity of words might not always be a good indicator for a trainable pattern).