I am working on a 'two-class classification of multivariate time-series' problem. I used two different approaches:
1) Manual time-series feature engineering (such as slope, intercept, variance, etc. using tsfresh python library) followed by a multi-layer perceptron.
2) Conv1D followed by LSTM followed by Dense layers (keras/Python) without any feature engineering.
They both yield very similar results (~40% precision, ~95% recall).
Can I infer anything from the similarity of results from two different architectures?