I am working with simulated sequential data and the goal is to forecast that data. Long-short-term-memory (LSTM) is one of the most advanced models to forecast time series according to this post. I can imagine that it is a good model because of the memory-cells they use which are useful when learning of the past.
This paper discussed the use of CNN in time-series analysis. It says:
CNN is suitable for forecasting time-series because it offers dilated convolutions, in which filters can be used to compute dilations between cells. The size of the space between each cell allows the neural network to understand better the relationships between the different observations in the time-series .
It even outperformed LSTM:
A specific architecture of CNN, WaveNet, outperformed LSTM and the other methods in forecasting financial time-series .
I see more and more posts about the usage of CNN in combination with LSTM, but I can't find any information about the advantages and disadvantages of using these in combination.
This post (Advantages of CNN vs. LSTM for sequence data like text or log-files), it is asked about the advantages of CNN vs. LSTM. But I would like to know the advantages and disadvantages of adding CNN to LSTM for forecasting univariate sequential data? Or should you use one of the two algorithms?