Table of Contents

Enum BeveridgeNelsonAlgorithmType

Namespace
AiDotNet.Enums.AlgorithmTypes
Assembly
AiDotNet.dll

Represents different algorithm types for Beveridge-Nelson decomposition of time series data.

public enum BeveridgeNelsonAlgorithmType

Fields

ARIMA = 1

Uses ARIMA (AutoRegressive Integrated Moving Average) models to perform Beveridge-Nelson decomposition.

For Beginners: ARIMA stands for AutoRegressive Integrated Moving Average, which is a popular statistical method for analyzing and forecasting time series data.

This approach combines the Beveridge-Nelson decomposition with ARIMA modeling to better capture the patterns in your data. It's like having a more sophisticated tool that can detect subtle patterns in your time series.

Think of it as using a high-powered microscope instead of a magnifying glass - you can see more details and make better distinctions between what's permanent and what's temporary in your data.

The ARIMA approach is particularly useful when your data has complex patterns that the standard method might miss.

Multivariate = 2

Extends the Beveridge-Nelson decomposition to handle multiple related time series simultaneously.

For Beginners: The Multivariate approach allows you to analyze multiple related time series at once, taking into account how they influence each other.

Imagine you're analyzing both unemployment rates and inflation - these economic indicators affect each other, so analyzing them together can reveal insights that looking at each separately would miss.

This is like studying an ecosystem rather than a single species - you get a more complete picture by seeing how different elements interact.

The Multivariate approach is more complex but can be much more powerful when you have multiple related data series that might share common permanent and temporary components.

Standard = 0

The standard implementation of the Beveridge-Nelson decomposition algorithm.

For Beginners: The Standard approach implements the original Beveridge-Nelson method as described in their 1981 paper. It uses statistical techniques to identify which changes in your data are likely to be permanent and which are likely to be temporary.

This approach is like separating a river into its main channel (the permanent component that keeps flowing in the same direction) and its eddies and whirlpools (temporary fluctuations that eventually dissipate).

The standard method works well for many basic time series but may not capture all the complexities in more sophisticated data.

Remarks

For Beginners: Beveridge-Nelson decomposition is a technique used in economics and finance to separate time series data (like stock prices or GDP over time) into two main components:

  1. Permanent Component (Trend) - The long-lasting changes that persist indefinitely
  2. Temporary Component (Cycle) - The short-term fluctuations that eventually fade away

Unlike other decomposition methods that might look at regular patterns (like seasonality), Beveridge-Nelson focuses on distinguishing between changes that will have lasting effects versus those that will eventually reverse.

For example, when analyzing a company's stock price:

  • A permanent component might be fundamental improvements in the company's business model
  • A temporary component might be short-term market excitement that will eventually subside

This enum lists different algorithmic approaches to performing this type of decomposition.

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