Experimental Data for Nonlinear Time Series Forecasting via Recurrent Neural Network Optimized by Ising Machine

This dataset contains the experimental results and plotting data for the research paper. The provided data supports the comparative analysis of various recurrent neural network (RNN) training architectures. The dataset (data_plot.zip) includes: * Logistic Map Time-Series Data: The synthetic chaotic sequence generated with a bifurcation parameter r=3.65. It consists of a training set of 950 points and a testing set of 200 points. Model Prediction Results: Comparative experimental trajectories for the following methods: * Standard RNN based on Backpropagation Through Time (BPTT-RNN). * Reservoir Computing based on Ridge Regression (Ridge-RC). * The proposed Ising-based RNN (Ising-RNN) using Simulated Annealing (SA) and Simulated Ising Machine (SIM) solvers. * RMSE Statistical Data: The Root Mean Square Error (RMSE) values calculated as a function of prediction time steps for all evaluated models. Purpose: This data is released to ensure the reproducibility of the findings and to provide a benchmark for next-generation neural computing systems powered by Ising machines.