Interface ITimeSeriesDecomposition<T>

Namespace

AiDotNet.Interfaces

Assembly

AiDotNet.dll

Defines methods and properties for decomposing time series data into its component parts.

public interface ITimeSeriesDecomposition<T>

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

Remarks

Time series decomposition breaks down a sequence of data points into underlying patterns such as trend, seasonality, and residual components.

For Beginners: Time series decomposition is like taking apart a complex signal (like sales data over time) into simpler pieces that are easier to understand. Imagine your store's sales throughout the year - decomposition helps you separate:

• The overall growth trend (are sales generally increasing?)
• Seasonal patterns (higher sales during holidays?)
• Day-to-day random variations

This makes it easier to understand what's really happening in your data and make better predictions.

Properties

TimeSeries

Gets the original time series data that was decomposed.

Vector<T> TimeSeries { get; }

Property Value

Vector<T>

Remarks

This property provides access to the input data used for the decomposition.

For Beginners: This is simply your original data - the sequence of values over time that you wanted to analyze. For example, if you're analyzing daily temperature readings, this would be your complete list of temperature values in chronological order.

Methods

GetComponent(DecompositionComponentType)

Gets a specific component of the time series decomposition.

object? GetComponent(DecompositionComponentType componentType)

Parameters

componentType DecompositionComponentType

The type of component to retrieve (e.g., Trend, Seasonal, Residual).

Returns

object

The requested component data, or null if the component doesn't exist.

Remarks

This method retrieves a single component by its type.

For Beginners: This lets you look at just one specific pattern in your data. For example, if you only want to see the seasonal pattern in your sales data (like how much sales increase during holidays), you would use this method to get just that piece.

The component is returned as an "object" type, which means you'll need to convert it to the appropriate type (usually a Vector<T>) before using it.

GetComponents()

Gets all available decomposition components as a dictionary.

Dictionary<DecompositionComponentType, object> GetComponents()

Returns

Dictionary<DecompositionComponentType, object>

A dictionary mapping component types to their corresponding data.

Remarks

This method returns all the extracted components from the time series.

For Beginners: This gives you all the pieces that make up your data, organized by type. Common components include:

• Trend: The long-term direction (going up, down, or staying flat)
• Seasonality: Regular patterns that repeat (like higher sales every weekend)
• Cycle: Longer patterns that don't have a fixed frequency
• Residual/Noise: The random variations that don't fit any pattern

Each component helps you understand a different aspect of what's happening in your data.

HasComponent(DecompositionComponentType)

Checks if a specific decomposition component is available.

bool HasComponent(DecompositionComponentType componentType)

Parameters

componentType DecompositionComponentType

The type of component to check for (e.g., Trend, Seasonal, Residual).

Returns

bool

True if the component exists; otherwise, false.

Remarks

This method determines whether a particular component was extracted during decomposition.

For Beginners: Not all decomposition methods extract all possible components. This method lets you check if a specific component is available before trying to use it.

For example, some simple decomposition methods might not separate cyclical patterns from the trend. You can use this method to check if the "Cycle" component exists before trying to analyze it.