Although there is an abundance of historical stock data for ML to train on, a high noise to signal ratio and the multitude of market conditions cause poor predictions of stock prices.
Let us consider a convenient solution to overcome these problems in the form of Long Short Term Memory (LSTM). LSTMs provide us with a large range of parameters such as learning rates, and input and output biases. Hence, no need for fine adjustments. The complexity to update each weight is reduced to O(1) with LSTMs, similar to that of Back Propagation Through Time (BPTT), which is an advantage.
In LSTM NN, the past inputs to the model leave a footprint. Hence, LSTM is great tool for anything that has a sequence.
Digital High Science 