• Classification and regression trees (CART): First, use supervised learning to teach trees to classify anomaly and non-anomaly data points. To realise this task, labelled anomaly data points are needed. Second, use unsupervised learning to teach CART to predict the next data point in the series and have some confidence interval or prediction error as in the case of the STL decomposition approach. If applying neural network, LSTM allows modelling the most sophisticated dependencies in time series as well as advanced seasonality dependencies.
  • Auto-Regressive Integrated Moving Average (ARIMA): Based on an approach that several points from the past generate a forecast of the next point with the addition of some random variable, which is usually white noise (just like a forward-moving window). Seasonal ARIMA has two important factors, trend, and seasonality.
  • Auto ARIMA returns the best set of parameters for the algorithm in our specified range.

Current Progress

The first approach that I implemented was based on the ARIMA approach using a rolling time window over historic data to calculate a boundary outside of which anomalies could be identified. A trial of the rolling-time window is illustrated as follows:


Thanks for your time and reading. In this blog, I included some literature I found and the current trial experiments. These contents will be updated and replenished throughout the project. Therefore, if you have any thoughts, please don’t hesitate to contact me. I will be very happy to hear your thoughts so I can improve the outcome of this project.



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The Data Analysis Bureau

The Data Analysis Bureau

We are a Data Science and Data Engineering Innovation Agency specialising in Machine Learning.