Discussing the article: "Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks"
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Check out the new article: Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks.
In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.
Both LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) neural networks are powerful tools for traders seeking to leverage advanced time-series forecasting models. While LSTMs provide a more intricate architecture that excels at capturing long-term dependencies in market data, GRUs offer a simpler and more efficient alternative that can often match the performance of LSTMs with less computational costs.
These Timeseries deep learning models(LSTM and GRU), have been utilized in various domains outside forex trading such as weather forecasting, energy consumption modeling, anomaly detection, and speech recognition with great success as usually hyped however, In the forever-changing forex market I can not guarantee such promises.
Author: Omega J Msigwa