Data science: “Anomaly Detection in Time Series Data, Techniques and Applications”.
The process of discovering unexpected or odd patterns in data is known as anomaly detection, sometimes known as outlier detection. Time series data, or data collected over time, is especially well suited for anomaly identification since it frequently contains patterns that are not readily apparent. Time series data anomaly detection is significant because it can disclose important information about the underlying system or process being researched. We'll take a deep look at the different methodologies and applications for detecting anomalies in time series data. We always simplify the theoretical concept. Enjoy!
Techniques for Anomaly Detection in Time Series Data: Anomaly detection in time series data can be accomplished using a variety of techniques, including statistical methods, machine learning methods, and deep learning methods.
1. Statistical Methods
Statistical methods for detecting anomalies in time series data assume that the data is created by a probability distribution. The Z-score approach, which measures the distance of each data point from the mean in terms of standard deviations, is one prominent way. The moving average method computes the average of a given number of prior data points and compares it to the current data point.
Example
The Z-score method is a statistical method for detecting anomalies in time series data. The Z-score method calculates the standard deviations of each data point from the mean. Anomalies are data points that deviate from the mean by more than a certain number of standard deviations.
The Z-score is calculated as follows:
(x - mean) / standard deviation = Z
Where x is the data point, mean is the data mean, and the standard deviation is the data standard deviation.
2. Machine Learning Methods
The goal behind machine learning methods for anomaly identification in time series data is to use a model to learn the patterns in the data. The k-nearest neighbours’ approach, which compares the current data point to the k-closest data points in the training set, is one popular method. Another way is the support vector machine method, which uses a hyperplane to divide the data into multiple classes.
Example: Support Vector Machine (SVM) for anomaly detection.
SVM can detect anomalies by treating the data as a two-class problem, with one class representing normal data points and the other class representing anomalous data points. The key idea behind using SVM for anomaly detection in time series data is to extract a set of feature vectors from the time series data using a sliding window. Each feature vector is a collection of features extracted from a fixed-length time-step window. The SVM classifier is then trained on these feature vectors to learn the time series data's normal behaviour. Following training, the classifier can be used to predict the class label of new feature vectors, and any vectors predicted to belong to the anomalous class are considered anomalies.
3. Deep Learning Methods
Deep learning methods for detecting anomalies in time series data are based on the idea of utilising neural networks to learn data patterns. The autoencoder method, which employs a neural network to discover the underlying structure of the data, is one prominent approach. Another approach is the long short-term memory (LSTM) method, which employs a form of neural network that is especially well adapted to time series data.
Example
A Long Short-Term Memory (LSTM) network is a practical example of a machine learning method for detecting anomalies in time series data. Because they can remember previous information in the sequence, LSTM networks are a type of recurrent neural network that is well-suited for time series data. A practical implementation of the LSTM network can be carried out in Python using the Keras library. We have a tutorial on this under the machine learning page.
Applications of Anomaly Detection in Time Series Data
Anomaly detection in time series data has numerous applications in finance, healthcare, manufacturing, and transportation.
1. Finance: Time series anomaly detection can be used to detect fraudulent financial transactions such as credit card fraud or insider trading. It can also be used to detect unusual market movements, such as sharp fluctuations in stock prices or currency exchange rates.
2. Healthcare: Anomaly detection in time series data can be used to track patients' vital signs such as heart rate and blood pressure. It can also be used to detect unusual patterns in electronic medical records, such as changes in lab results or medication prescriptions that are not expected.
3. Manufacturing: Anomaly detection in time series data can be used to monitor the performance of manufacturing equipment such as machines or robots. It can also be used to detect abnormal patterns in production data, such as unexpected changes in the number of defective products or scrap materials.
4. Transportation: Anomaly detection in time series data can be used to monitor the performance of transportation systems such as roads or trains. It can also be used to detect abnormal patterns in traffic data, such as unexpected changes in the number of vehicles on the road or the speed of vehicles.
Conclusion
An important and widely used technique for identifying unusual patterns in data is anomaly detection in time series data. Anomaly detection can be accomplished using a variety of techniques, including statistical methods, machine learning methods, and deep learning methods. Each of these techniques has advantages and disadvantages, and the technique used will be determined by the specific problem being solved as well as the characteristics of the data.