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CodeSignal

Introduction to RNNs for Time Series Analysis

via CodeSignal

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Overview

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.

Syllabus

  • Unit 1: Understanding Time Series Data and RNNs
    • Handling Time Series Data
    • Visualize Time Series Trends
  • Unit 2: Preparing Time Series Data for RNNs
    • Standardizing Time Series for RNN Success
    • Normalizing Passenger Data with MinMaxScaler
    • Creating Sequences for Time Series Prediction
    • Building a Complete RNN Data Pipeline
  • Unit 3: Building and Evaluating a Basic RNN Model
    • Building Your First RNN Model
    • Training Your First RNN Model
    • Visualizing RNN Training Loss Curves
    • Evaluating RNN Model Performance
    • Experimenting with RNN Model Parameters
  • Unit 4: Extending RNNs for Time Series Classification Tasks
    • Preparing Time Series Data for Classification
    • Converting Labels for Neural Networks
    • Building RNN Models for Classification
    • Training and Evaluating RNN Classifiers

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