Before revision:  We find CLA out-performs simple LSTM and FFNN learners and CLA based on a sliding window (CLA-FFNN) out-performs a LSTM (CLA-LSTM) implementation. While for memory-addressing, ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.
After revision:  We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.