Anomaly Detection in SwiftUI
Learn how to identify patterns in time-series data that deviate from expected behavior with SwiftUI
Anomaly detection in time series data involves identifying patterns or data points that deviate from the expected behavior. In this article, we will explore how to use SwiftUI to detect anomalies in time series data.
SwiftUI is a user interface framework for Apple platforms that provides an easy-to-use declarative syntax for building complex interfaces. With its powerful tools for data visualization, SwiftUI is a great choice for building applications that need to analyze and present time series data.
Lets’ look at how we can use SwiftUI for anomaly detection in time series data.
Prepare the data
Before we can start detecting anomalies, we need to prepare the time series data. This may involve cleaning and pre-processing the data to remove any missing values or outliers.
//Example data preparation
let data: [Double] = [1, 2, 3, 4, 5, 6, 7, 10, 12, 14, 15, 16, 17, 18, 19, 20]Define the parameters for anomaly detection:
Next, we need to define the parameters for anomaly detection. This may include the type of…
