Introduction to RNN for Time Series Forecast with TensorFlow

Overview

Learn to preprocess, model, and forecast time series data using RNNs, LSTMs, and GRUs in TensorFlow. Build, evaluate, and optimize models for univariate and multivariate forecasting and classification tasks.

Syllabus

Course 1: Introduction to RNNs for Time Series Analysis
Course 2: Handling Multivariate Time Series with RNNs
Course 3: Time Series Forecasting with LSTMs
Course 4: Time Series Forecasting with GRUs

Courses

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1 hour
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Learn how to build and evaluate RNN, LSTM, and GRU models for time series forecasting. This hands-on course covers univariate and multivariate data, classification, and advanced deep learning techniques for improved accuracy.
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This course extends the concepts from the first course by introducing multiple time series inputs. It covers how to preprocess, structure, and train RNN models using **two related time series features** from the **Air Quality dataset**. It also includes model evaluation techniques to assess forecasting accuracy.
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This course focuses on LSTM networks, a powerful extension of RNNs that handle long-term dependencies better than simple RNNs. Learners will build, optimize, and evaluate LSTM models for time series forecasting.
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This course explores Gated Recurrent Units (GRUs) in TensorFlow for multivariate time series forecasting. We will build, evaluate, and apply advanced GRU techniques like Bidirectional GRUs, Attention Mechanisms, and Hybrid GRU-CNN models to improve forecasting accuracy.