DIY #21 - Step-by-Step Guide to Time Series Forecasting with RNN

2026-03-21 · Source: Machine Learning Pills · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article provides a hands-on guide to building and training a Vanilla Recurrent Neural Network (RNN) for time series forecasting using Keras and Scikit-Learn. It details the process of generating a synthetic noisy sine wave dataset, which mimics real-world seasonal data, and then preparing it for an RNN by scaling values between 0 and 1 using `MinMaxScaler` and reframing it into a supervised learning problem with a sliding window of 20 timesteps. The guide outlines the construction of a `Sequential` Keras model with a `SimpleRNN` layer (16 units, `tanh` activation) and a `Dense` output layer, followed by compilation with `mean_squared_error` loss and the `adam` optimizer. The model is trained for 30 epochs with a batch size of 16, and its performance is evaluated using Root Mean Squared Error (RMSE) on a sequentially split test set, achieving a low RMSE of 0.1221.

Key takeaway

For Machine Learning Engineers building time series forecasting models, this guide demonstrates a robust workflow for Vanilla RNNs. You should prioritize data preprocessing steps like scaling and sequential train/test splitting to ensure model integrity. Implementing a sliding window approach is critical for transforming raw time series into a suitable format for RNN input, enabling your model to learn temporal dependencies effectively and achieve accurate predictions on cyclical patterns.

Key insights

Vanilla RNNs effectively forecast cyclical time series data by learning temporal patterns through sequential processing.

Principles

• Scale time series data for neural networks.
• Reframing time series as supervised learning is crucial.
• Sequential train/test splits prevent data leakage.

Method

Generate synthetic cyclical data, scale it, reframe into `[samples, timesteps, features]` for RNN input, build a `SimpleRNN` model, train, and evaluate using RMSE.

In practice

• Use Keras `SimpleRNN` for basic sequence modeling.
• Apply `MinMaxScaler` for data normalization.
• Plot training/validation loss to monitor learning.

Topics

• Vanilla RNNs
• Time Series Forecasting
• Keras
• Data Preprocessing
• Neural Network Training

Best for: AI Student, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning Pills.