What have my models learnt?

I am doing a time series classification task. I used LSTM, Bi-LSTM. Bi-LSTM works a little bit better than single layer LSTM. And concatenating two Bi-LSTM outputs with another input gives me a better result.

But after all, what have my models leant? I actually don't think there are any patterns in this time series. How does LSTM give the outputs from these irregular data? Why this model works better than the other? Is it pure luck?