The Gated Recurrent Unit (GRU) is a type of Recurrent Neural Network (RNN) that is commonly used for time series forecasting tasks. GRUs are similar to LSTMs in that they also have gates that regulate the flow of information in the network, but they are simpler and faster to train than LSTMs.
The GRU has two gates: a reset gate and an update gate. The reset gate determines which parts of the previous hidden state should be forgotten, while the update gate determines which parts of the new input should be added to the hidden state. The hidden state is updated as follows:
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First, the reset gate is calculated based on the previous hidden state and the current input. The reset gate determines which parts of the previous hidden state to forget.
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Next, a candidate hidden state is calculated based on the current input and the reset gate. The candidate hidden state represents the new information that should be added to the hidden state.
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Finally, the update gate is calculated based on the candidate hidden state and the previous hidden state. The update gate determines how much of the candidate hidden state should be added to the previous hidden state to get the new hidden state.
The GRU architecture has been shown to be effective for various time series forecasting tasks, such as predicting stock prices, weather forecasting, and energy consumption. One of the advantages of GRUs is their ability to capture long-term dependencies in the data, which is important for time series forecasting tasks that involve long-term dependencies.
In summary, the GRU algorithm is a type of RNN that is commonly used for time series forecasting tasks. It uses reset and update gates to regulate the flow of information in the network and update the hidden state. GRUs have proven to be effective for various time series forecasting tasks, and they are simpler and faster to train than LSTMs.