Artificial Intelligence and Anomaly Detection

Anomaly Detection With LSTM Autoencoders

Unsupervised ML Approach for fund management

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Anomaly here to detect that, actual results differ from predicted results in price prediction. As we are aware that, real-life data is streaming, time-series data etc., where anomalies give significant information in critical situations. In the detection of anomalies, we are interested in discovering abnormal, unusual or unexpected records and in the time series context, an anomaly can be detected within the scope of a single record or as a subsequence/pattern.

Estimating the historical data, time-series based predictive model helps us in predicting future price by estimating them with the current data. Once we have the prediction we can use that data to detect anomalies on comparing them with actuals.

Let’s implement it and look at its pros and cons. Hence, our objective here is to develop an anomaly detection model for Time Series data. We will use neural-network architecture for this use case.

Let us load Henry Hub Spot Price data from EIA. We have to remember that, the order of data here is important and should be chronological as we are going to…