Building an LSTM Autoencoder in Keras, Detecting the anomalies with Autoencoders in time series data, and Creating interactive charts and plots with Plotly and Seaborn. Designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index.

Dataset Used: S&P 500 Index Dataset

1: Import Libraries

2: Load and Inspect the S&P 500 Index Data

3: Data Preprocessing

4: Temporalize Data and Create Training and Test Splits

5: Build an LSTM Autoencoder

6: Train the Autoencoder

7: Plot Metrics and Evaluate the Model

8: Detect Anomalies in the S&P 500 Index Data