This project shows how to find anomalies in financial time series data, specifically the stock values of Apple (AAPL), using a LSTM Autoencoder. Stock price anomalies may be a sign of major market events like crashes, surges in volatility, or other unusual activity. The model identifies these anomalies based on reconstruction error, which highlights unusual patterns in the data that deviate from historical trends.