Enum AdditiveDecompositionAlgorithmType

Namespace

AiDotNet.Enums.AlgorithmTypes

Assembly

AiDotNet.dll

Represents different algorithm types for additive decomposition of time series data.

public enum AdditiveDecompositionAlgorithmType

Fields

ExponentialSmoothing = 1

Uses exponential smoothing to decompose time series data, giving more weight to recent observations.

For Beginners: Exponential Smoothing is like a weighted average that gives more importance to recent data and less importance to older data. It's similar to how you might naturally pay more attention to what happened yesterday than what happened last month.

This method is particularly useful when recent changes in your data are more important for predicting future values. For example, when forecasting product demand, recent sales trends might be more relevant than older patterns.

MovingAverage = 0

Uses a moving average approach to decompose time series data.

For Beginners: The Moving Average method works by calculating averages over fixed windows of time to smooth out short-term fluctuations and highlight longer-term trends. It's like looking at the "big picture" by averaging out the day-to-day noise in your data.

For example, instead of looking at daily sales which might vary a lot, a 7-day moving average would give you the average sales for each 7-day period, creating a smoother line that makes the overall trend easier to see.

STL = 2

Uses Seasonal and Trend decomposition using Loess (STL) to break down time series data.

For Beginners: STL (Seasonal and Trend decomposition using Loess) is a more sophisticated method that can handle complex seasonal patterns that might change over time.

Imagine you're analyzing ice cream sales over several years. Not only do sales increase in summer and decrease in winter (seasonality), but maybe the overall popularity of ice cream is growing year by year (trend). STL can separate these patterns even if the summer peaks are getting higher each year.

"Loess" refers to a special statistical technique used in this method that helps fit smooth curves to different parts of your data.

Remarks

For Beginners: Additive decomposition is a technique used to break down time series data (data collected over time, like daily temperatures or monthly sales) into separate components:

1. Trend - The long-term direction of the data (going up, down, or staying flat over time)
2. Seasonality - Regular patterns that repeat at fixed intervals (like higher sales during holidays)
3. Residual - The random fluctuations left after accounting for trend and seasonality

It's called "additive" because we assume these components add together to form the original data: Original Data = Trend + Seasonality + Residual

This enum lists different algorithms that can perform this decomposition, each with its own approach to separating these components.