Table of Contents

Enum EMDAlgorithmType

Namespace
AiDotNet.Enums.AlgorithmTypes
Assembly
AiDotNet.dll

Represents different algorithm types for Empirical Mode Decomposition (EMD).

public enum EMDAlgorithmType

Fields

CompleteEnsemble = 2

Uses the Complete Ensemble Empirical Mode Decomposition (CEEMD) algorithm.

For Beginners: Complete Ensemble EMD is an enhanced version of EEMD that adds noise in pairs (positive and negative) to ensure that the added noise cancels out completely in the final result.

While EEMD adds random noise many times and averages the results, some residual noise can still remain in the final decomposition. CEEMD solves this by adding pairs of noise with opposite signs, ensuring that the noise cancels out perfectly when averaged.

Think of it like taking multiple photographs of a scene with different random camera shakes, but making sure that for every photo with a shake to the left, there's another with an equal shake to the right. When you average all the photos, the shakes cancel out completely, giving you a perfectly clear image.

CEEMD provides more accurate decompositions than EEMD while still solving the mode mixing problem. It's particularly valuable when high precision is required, such as in medical signal analysis or high-frequency financial data analysis.

Ensemble = 1

Uses the Ensemble Empirical Mode Decomposition (EEMD) algorithm.

For Beginners: Ensemble EMD improves on the standard algorithm by adding small amounts of noise to the original signal and performing multiple decompositions.

Imagine you're trying to find a path through a foggy forest. If you make the journey just once, you might get lost. But if you make the journey many times with slightly different starting points, and then average all your paths, you'll likely find the best route. EEMD works similarly by adding random noise to the signal multiple times and then averaging the results.

This approach helps solve the "mode mixing" problem of standard EMD, where components with similar frequencies can get mixed together. By adding noise and averaging multiple decompositions, EEMD can better separate these mixed components.

EEMD is particularly useful for analyzing complex signals like climate data, biomedical signals, or financial time series where different patterns may overlap.

Multivariate = 3

Uses the Multivariate Empirical Mode Decomposition (MEMD) algorithm.

For Beginners: Multivariate EMD extends the EMD concept to handle multiple related signals simultaneously.

While standard EMD, EEMD, and CEEMD work on a single data series (like temperature over time), MEMD can analyze multiple related data series together (like temperature, humidity, and pressure over time).

Imagine you're analyzing a symphony orchestra again, but now instead of just separating instruments, you want to identify when all instruments are playing the same melody or theme, even if they're playing at different volumes or with slight variations. MEMD helps you find these common patterns across multiple channels of information.

This is particularly useful in:

  1. Brain signal analysis (EEG) where data comes from multiple sensors

  2. Environmental studies where multiple variables interact

  3. Financial markets where different stocks or indicators may show related patterns

  4. Motion capture data where movements in different dimensions are related

MEMD preserves the relationships between different channels of data, allowing you to discover patterns that might be missed if each channel were analyzed separately.

Standard = 0

Uses the standard Empirical Mode Decomposition algorithm.

For Beginners: The Standard EMD is the original version of the algorithm that decomposes a signal into a collection of Intrinsic Mode Functions (IMFs).

It works by identifying local extremes (peaks and valleys) in your data, connecting them with smooth curves, and then subtracting these curves from the original data. This process is repeated multiple times until the signal is fully decomposed.

Think of it like peeling an onion - you remove one layer at a time, with each layer representing a different oscillation pattern in your data.

The Standard EMD works well for many applications but has some limitations with certain types of data, particularly when dealing with signals that have similar frequencies appearing at different times (known as mode mixing).

Remarks

For Beginners: Empirical Mode Decomposition (EMD) is a technique used to break down complex data signals into simpler components called Intrinsic Mode Functions (IMFs).

Imagine you're listening to an orchestra. The music you hear is a complex mixture of sounds from many instruments playing together. EMD is like having a special ability to hear each instrument separately, even though they're all playing at once. It helps you understand how each instrument contributes to the overall music.

In data analysis and AI, EMD helps us:

  1. Analyze non-stationary data (data that changes its statistical properties over time), like stock market prices, weather patterns, or brain signals

  2. Extract meaningful patterns from noisy data

  3. Identify hidden cycles or trends in complex time series data

  4. Preprocess data for machine learning models to improve their performance

Unlike Fourier transforms (another common technique) which assume data patterns repeat regularly, EMD adapts to the data itself, making it particularly useful for real-world data that often contains irregular patterns and sudden changes.

This enum lists different variations of the EMD algorithm, each with specific strengths for different types of data analysis problems.

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