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WanjaMIKE
Proposal Submission
Proposed title of article
[Machine Learning] Anomaly Detection Model on Time Series data using Isolation Forest
Proposed article introduction
A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Time series forecasting is the use of a model to predict future values based on previously observed values. Timeseries dataset may have anomalies or outliers that may lead to inconsistent results. Anomalies/outliers are observations or data points that deviate from a dataset’s normal behavior.
Anomalies in data are also called standard deviations, outliers, noise, novelties, and exceptions. Anomaly detection is the process of finding outliers in a given dataset. Outliers are the data objects that stand out amongst other objects in the dataset and do not conform to the normal behavior in a dataset. Anomaly detection identifies rare events, items, or observations that are suspicious because they differ significantly from standard behaviors or patterns.
Isolation forest is a machine learning algorithm for anomaly detection. It's an unsupervised learning algorithm that identifies anomalies by isolating outliers in the data. Isolation Forest is based on the Decision Tree algorithm.
Key takeaways
Article quality
This tutorial will explain time series analysis and decomposition in detail. We will discuss the decomposable components such as trends, cyclic, and seasonality components. The tutorial will also discuss the two types of anomalies: Contextual and global anomalies. We will deal with contextual anomalies in detail and implement an Isolation Forest algorithm to detect the anomalies. Finally, we will plot a visualization graph to show all the anomalies in the dataset. This will help to inconsistent results in model training and forecasting.
References
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