Detection of anomalies (aka outlier analysis) is a data mining phase that detects events, data points and/or findings that deviate from the normal behavior of a dataset. Critical accidents, such as a potential opportunity, or technological glitches, as in a change in customer behavior, may be indicated by anomalous data. To automate anomaly detection, machine learning is increasingly being used.
A few of the most common methods used for anomaly detection and prediction are discussed below. We also highlight the benefits of using certain recommended methods as best practices for timely nailing of such anomalies.
Tips to Detect Anomaly in Your Data
Start Your 30-Day FREE TRIAL with Data Science Academy to Launch Your Career in Data Science. Connect with our experts to learn more about our IT courses.
Manual vs Cognitive Approach
In detecting the extreme value points or the outliers that cause anomalies, a manual approach to anomaly detection is fine. In order to train and construct machine learning models, it only relies on sample data. However, choosing data samples cannot include all errors or signals, and anomalies are unusual events.
The main flaw here is that in essence it is responsive, and previous anomalies may not necessarily be indicative of potential issues. For the entire time series of data, it often attempts to match a single behavioral trend model. For all entities, this resulted in creating a single predictive model.
As each entity occupies numerous steps, a single model does not describe them. Due to environmental conditions, any person is different in the real world, so a particular system cannot learn and predict for everyone. In addition, anomalies are also common values that are out of order and may be the critical ones. Therefore, it is not helpful to merely classify anomalies, but it is important to assign findings based on importance.
On the other hand, a cognitive method to anomaly detection and prediction incorporates a machine-first technique. It provides a process where the algorithms learn the field from the details and expert input on the subject matter. To distinguish common and distinct entities, the process begins by generating specific data signatures. In order to learn and model normal states and recognize anomalous states, it collects data patterns. For all assets, these prove to be the anti-patterns.
Top-Down traditional approach vs Bottom-Up approach
Different factors are determined for each sensor in the traditional top-down methodology. To describe state space, all these features produced from each sensor are then put together. Here the motivation behind producing features is to capture various features of each sensor in different operating stages.
For instance, the rate of change function will become very useful if there is an upward or downward movement in the signal. Similarly, features from the frequency framework can be very helpful where there is a well-defined periodicity in the values of a particular sensor. Although it would be beneficial to produce each feature, its usefulness is normally local to a signal with a particular characteristic. In a global setting, it cannot be as useful until the signal stops showing such characteristics. So, if a specific sensor comes out of the stage where a positive or negative trend has followed, the features of the rate of change will mostly be nearly identical to zero.
Moreover, all sensors cannot show the same characteristics. This makes the specific sensor less usable for some of the features being generated. However, as engineered features are applied to the entire data, these calculations of features result in the dimension of state space being increased. With increasing numbers of sensors and as each sensor goes through various phases of operations, this sparseness of information will normally increase. The state-space of the machine is, however, built in the bottom-up method, recognizing that each sensor is a mapping of a portion of the data generation dynamic process. In addition, it understands that as the environment/process/configuration on the system is evolving, sensors will move through various phases.
Sensors are broken into their respective phases prior to determining the state space of the system. Therefore, it does not depend on feature engineering to implicitly code knowledge from various phases of operations. This also takes into consideration the reality that only in a particular sub-group of sensors can anomalies appear. This can be harder to identify in the space of the machine state especially when there are a large number of sensors. At the same time, at the individual sensor level, system-level anomalies arising from the interaction of sensors would be difficult to detect.
Detection and Prediction of Rule-based/Supervised vs Unsupervised Anomaly
Two basic approaches, rule-based or supervised machine learning detection methods, can be used for anomaly detection. Basic rules that identify an anomaly and assign thresholds and limitations are designed to define rule-based structures. "Usually, they rely on industry experts' experience and are perfect for detecting known anomalies." We are familiar with these known anomalies, as we understand what is usual and what is not.
One of the big weaknesses of rule-based systems is that when patterns change, they don't adapt automatically. A new model would have to be developed with labeled data per time to learn new patterns. As a consequence, these systems are not appropriate for high-velocity dynamic data. In addition, the method of data labeling itself can be manually intensive, vulnerable to error and can lead to low performance of the model. Basically, capturing the "unknown unknowns" with supervised methods is quite a problem.
Unsupervised learning can help to deduce irregular trends and warn operators of plants accordingly. These machine learning algorithms continuously predict what is going to happen next in the metric stream of data, much like the human brain attempting to anticipate the next note in a melody. They give a likelihood score for every estimation by being efficient enough to predict many data trends at once. The learning algorithms evaluate their projection to the current input for each new metric data point arriving, to see whether or not the prediction is correct.
When determining the right solution for your industry, what are the crucial considerations to bear in mind? While there are several anomaly detection and prediction methods, how does one get decided to start? In my view, with various methods to anomaly detection and prediction, a data science certification is very useful.
Enroll in our Data Science Training Program to get started. Start your 30-day free trial today!