Iterative Forecasting which is Predicting One Step at a Time 2- Direct Multi-Step Forecasting with RNN 3- Seq2Seq Models for Time Series Forecasting – day 61

Mastering Time Series Forecasting with RNNs and Seq2Seq Models: Detailed Iterations with Calculations, Tables, and Method-Specific Features Time series forecasting is a crucial task in various domains such as finance, weather prediction, and energy management. Recurrent Neural Networks (RNNs) and Sequence-to-Sequence (Seq2Seq) models are powerful tools for handling sequential data. In this guide, we will provide step-by-step calculations, including forward passes, loss computations, and backpropagation for two iterations across three forecasting methods: Iterative Forecasting: Predicting One Step at a Time Direct Multi-Step Forecasting with RNN Seq2Seq Models for Time Series Forecasting Assumptions and Initial Parameters For consistency across all methods, we’ll use the following initial parameters: Input Sequence: Desired Outputs: For Iterative Forecasting and Seq2Seq: For Direct Multi-Step Forecasting: Initial Weights and Biases: Weights: (hidden-to-hidden weight) (input-to-hidden weight) will vary per method to accommodate output dimensions. Biases: Activation Function: Hyperbolic tangent () Learning Rate: Initial Hidden State: 1. Iterative Forecasting: Predicting One Step at a Time In iterative forecasting, the model predicts one time step ahead and uses that prediction as an input to predict the next step during inference. Key Feature: During training, we use actual data to prevent error accumulation, but during inference, predictions are fed back into…