Step-by-Step Explanation of RNN for Time Series Forecasting – part 6 – day 60

RNN Time Series Forecasting Step-by-Step Explanation of RNN for Time Series Forecasting Step 1: Simple RNN for Univariate Time Series Forecasting Explanation:An RNN processes sequences of data, where the output at any time step depends on both the current input and the hidden state (which stores information about previous inputs). In this case, we use a Simple RNN with only one recurrent neuron. TensorFlow Code: Numerical Example:Let’s say we have a sequence of three time steps: . 1. Input and Hidden State Initialization:The RNN starts with an initial hidden state , typically initialized to 0. Each step processes the input and updates the hidden state: where: is the weight for the hidden state. is the weight for the input. is the bias term. is the activation function (hyperbolic tangent). Assume: Let’s calculate the hidden state updates for each time step: Time Step 1: Time Step 2: Time Step 3: Thus, the final output of the RNN for the sequence is . PyTorch Equivalent Code: — Step 2: Understanding the Sequential Process of the RNN Explanation:At each time step, the RNN processes the input by updating the hidden state based on both the current input and the previous hidden state. This…