Time-series forecasting is essential in various domains, such as finance, healthcare, and logistics. Traditional statistical methods like ARIMA and exponential smoothing have served us well but have limitations in capturing complex non-linear relationships in data. This is where Recurrent Neural Networks (RNNs) offer an edge, providing a powerful tool for modeling complex time-dependent phenomena. This article aims to be a comprehensive guide to time-series forecasting using RNNs.

Offering a robust tool for modeling intricate time-dependent phenomena, this guide provides a detailed overview of time-series forecasting using RNNs, covering everything from setting up your environment to building and evaluating an RNN model.

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