The reliability of analyses and projections relies heavily on time series data, which is compiled over a period of time and then updated at regular intervals. Anomalies are unusual occurrences or patterns, whereas outliers are extreme data values that depart far from the mean. Maintaining data integrity and making well-informed judgements need efficient detection and treatment of anomalies and outliers. This article will examine many methods, analyses, and algorithms for locating and handling outliers and anomalies in time series data. Test and algorithm are two examples of keywords that will appear often.
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UNDERSTANDING ANOMALIES AND OUTLIERS
Anomalies
Data points or patterns that deviate noticeably from the norm in a time series are said to be anomalies. Unusual occurrences, inaccurate data gathering, faulty sensors, and other environmental influences may all contribute to this. There is a wide variety of anomalies that may occur.
Point Anomalies:
Distinct deviations of a single data point from the norm.
Contextual Anomalies:
Statistics that seem typical under one set of conditions but out of place under another.
Collective Anomalies:
The collective behaviour of a group of data items that, taken separately, may seem typical.
Outliers
Statistical outliers are numbers that significantly deviate from the mean or median. Data input mistakes, faulty measurements, and unusual occurrences are all potential causes. The presence of outliers may skew statistical analysis and provide misleading results and predictions.
TECHNIQUES FOR DETECTING ANOMALIES AND OUTLIERS
1. Visual Inspection
Outliers and irregularities may be seen most easily via a visual examination. Box plots, scatter plots, and time series plots are all great tools for spotting outliers in a data set. But it's subjective and may not work well with big data.
2. Statistical Tests
There are a number of statistical tests that may be used to look for irregularities and outliers. Common diagnostic procedures include:
Z-Score Test:
Calculates the number of standard deviations from the mean for a given data point. Data points with a high absolute z-score may be anomalies.
Modified Z-Score Test:
An improved Z-Score test that performs well with heavy-tailed data.
Grubbs' Test:
Finds the one weird data point in a set.
Hampel Identifier:
Finds extreme values in data where they exist.
MAD (Median Absolute Deviation):
A reliable index of dispersion for picking out data points that are significantly out of the norm.
3. Time Series Decomposition
Decomposing a time series into its component parts, such as trend, seasonality, and residuals, is what is meant by "decomposition." Anomalies may be spotted by examining the residuals (the gap between the observed and predicted values).
4. Machine Learning Algorithms
Anomaly and outlier detection may be accomplished with the use of machine learning techniques like Isolation Forest, One-Class SVM, and Autoencoders. Algorithms that analyse data for anomalies may "learn" from the data's typical patterns. They shine in situations with several variables and extensive data.
5. Moving Average and Exponential Smoothing
In order to spot anomalies in time series data, it might be useful to use moving averages or exponential smoothing. Outliers are points in the data that are very high or low relative to the smoothed values.
TECHNIQUES FOR HANDLING ANOMALIES AND OUTLIERS
1. Data Imputation
Data imputation methods may be used to replace missing or unusual data points with estimated values based on nearby data points when the missing or unusual data points can be traced back to human error in data input or measurement.
2. Data Transformation
Normalising the data and making it more robust against outliers and anomalies is a common goal of data transformation methods like log transformation and Box-Cox transformation.
3. Data Trimming
Data trimming is the process of excluding anomalous records from a dataset. Caution is advised while using this method, since it may result in the discarding of important data, particularly if the outliers are substantive.
4. Outlier Capping
In order to limit the number of false positives, outlier capping establishes a cutoff value. To reduce their impact on the study, these outliers are swapped for the highest and lowest values allowed.
5. Anomaly Detection Algorithms
Anomaly detection techniques are designed specifically for finding and handling outliers. These algorithms provide anomaly ratings that may be used to prioritise abnormalities according to impact.
CONCLUSION
In order to preserve data quality and guarantee precise analyses and projections, it is essential to properly deal with anomalies and outliers in time series data. Anomalies and outliers may be detected using a variety of methods, including visual examination, statistical tests, time series decomposition, and machine learning techniques. Data imputation, data transformation, data trimming, and outlier capping are just some of the methods that may be used after outliers have been spotted. In order to choose the most appropriate methods, analysts and researchers must first carefully analyse the nature of their time series data and the impact of anomalies and outliers on their findings. Data-driven decision making and increased confidence in the use of insights from data collected across several fields benefit from effective management of anomalies and outliers.
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