Time Series of Price Anomaly Detection with LSTM

Autoencoders are an unsupervised learning technique, although they are trained using supervised learning methods. The goal is to minimize reconstruction error based on a loss function, such as the mean squared error. In this post, we will try to detect anomalies in the Johnson & Johnson's historical stock price time series data with an LSTM autoencoder. The data can be downloaded from Yahoo Finance. The time period I selected was from 1985–09–04 to 2020–09–03.