Anomaly detection is a crucial part of the quality assurance process and sophisticated data science, all its own. The method of finding outliers in any given set of data, allows users to recover data objects that stand out among other objects in the data set.
Where accuracy is required, anything that doesn’t match the normal behaviour of a data set is undesirable. This is where anomaly detection algorithms get their chance to shine. Because, once we’ve identified the problem, you can start making improvements and future proof your systems.
But how does anomaly detection work? What are some of the leading technologies in this field? And how do you stand to benefit from it? Join me today, as I break down this critical process, and get ready to demand more from your data.
What Is Anomaly Detection?
In the world of data mining, it’s essential to be accurate. Anomaly detection identifies data points and events that qualify as anomalous behaviours.
Unusual data is one of the first indicators of significant events that you may want to keep an eye on. Technical flaws and opportunities for improvements are both more common than you’d expect. Anomaly detection, meanwhile, lets you spot them from an early stage. Changes in consumer behaviour, for instance, are an opportunity to change the focus of your data mining. Technical glitches, meanwhile, may not prevent your data mining from moving forward but can hamper your accuracy.
In the quest for improved anomaly detection, machine learning is also an increasingly popular option.
And The Benefits?
Anomaly detection is an investment in the consistency and quality production of a service:
– Monitoring a full selection of data sources, from user logs and devices to networks, servers, meters and site for full access.
– Quickly identifying any zero-day threats and potential problems or faulty machinery.
– Using historical data to detect energy demand and consumption anomalies.
– Analyzing response time for better demand response, as well as live analysis of market data.
An automatic encoder uses principles similar to statistical analysis, but with a few differences. This style of artificial neural network learns efficient data coding without any need for supervision.
The goal of an automatic encoder is, typically, to learn the representation of a data set for the purposes of reducing dimensionality. The encoder itself tries to generate representations as close to the original input as possible.
Taking a closer look, autoencoders in their simplest forms are “feed-forward”, with node connections not forming a cycle. Non-repeatable neural networks are actually very similar to various single-layer perceptrons. They create multi-layer perceptrons (MLPs), with input and output layers. These also feature multiple hidden connecting layers. Even so, the output layer will have the same number of nodes as the input layer, ideal for reconstructing its input.
With growth in the scope of data, developing models to recognize standard behaviour has become difficult.
Deep learning goes a long way to rectifying this. Encoders learn how to create internal representations of any given input data. Decoders, meanwhile, are attempting to reconstruct the original input. They accomplish this by working from this internal representation, making it extremely important.
Now, the exact techniques used for encoding and decoding can and do vary from model to model. What matters more is the advantage. This is in learning standard input data distribution before measuring anomalies.
One of the most basic characteristics of big data is that it is, well, big. Large volumes of information of all sorts is created and uploaded over short periods of time. This data exists across connected devices, from cell phone networks to the Internet of things, in our case, up to 5-minute meter data, or near real-time market data.
Obviously, with so many balls in the air at once, things can get lost in translation. With that in mind, there are numerous benefits to anomaly detection. Early detection of an issue allows businesses and services to intervene before things get out of hand. This can save you a bill shock, failing machinery, maintenance control and much more.
When it comes to anomaly detection, comprehensive data verification is the name of the game. Identifying unexpected items or events in a data set, this process allows users to take those standard anomalies and address them.
Anomaly detection usually applies to unlabeled data, specifically. The two basic assumptions, here, are that:
- exceptions rarely occur in any standard data set
- functionality with these instances will differ significantly from normal
With this methodology in place, it’s possible to completely turn any dataset around for better performance.
If you want to learn how we implement all this and more to help you manage your energy efficiently, contact us, or chat with Us on the right bottom corner of the screen.
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Author: Oscar Omegna (formerly at EnergyByte)