trajceskijovan / Timeseries-anomaly-detection-using-LSTM

Timeseries anomaly detection using LSTM based on Johnson & Johnson (JNJ) daily data from 1985 to 2020

Repository from Github https://github.comtrajceskijovan/Timeseries-anomaly-detection-using-LSTMRepository from Github https://github.comtrajceskijovan/Timeseries-anomaly-detection-using-LSTM

Timeseries-anomaly-detection-using-LSTM

Timeseries anomaly detection using LSTM based on Johnson & Johnson (JNJ) daily data from 1985 to 2020

Background:

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems).

Jupiter Notebook:

https://github.com/trajceskijovan/Timeseries-anomaly-detection-using-LSTM/blob/main/Timeseries%20anomaly%20detection%20using%20LSTM.ipynb

Highlights:

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Timeseries anomaly detection using LSTM based on Johnson & Johnson (JNJ) daily data from 1985 to 2020


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