As much as it has become easier over the years to collect vast amounts of data across different sources, companies need to ensure that the data they’re gathering can bring value. To aid insight collection from the data, machine learning and analytics have become trending tools. Since these domains require real-time insights, an abundance of unwelcome data can create real issues.

Before decisions are made, and critically, before actions are taken, we must ask: are there anomalies in our data that could skew the results of the algorithmic analysis? If anomalies do exist, it is critical that we automatically detect and mitigate their influence. This ensures that we get the most accurate results possible before taking action.

In this post, we explore different anomaly detection approaches that can scale on a big data source in real-time. The tsmoothie package can help us to carry out this task. Tsmoothie is a python library for time series smoothing and outlier detection that can handle multiple series in a vectorized way. It’s useful because it can provide the techniques we needed to monitor sensors over time.

#time-series-analysis #editors-pick #anomaly-detection #data-science #machine-learning