Table of Contents

Enum SEATSAlgorithmType

Namespace
AiDotNet.Enums.AlgorithmTypes
Assembly
AiDotNet.dll

Represents different algorithm types for SEATS (Seasonal Extraction in ARIMA Time Series) decomposition.

public enum SEATSAlgorithmType

Fields

Burman = 2

Uses Burman's variant of the SEATS algorithm, which focuses on robust seasonal adjustment.

For Beginners: The Burman approach is a modified version of SEATS that emphasizes robustness and practical applicability, especially for data with outliers or structural changes.

Imagine you're trying to find patterns in weather data that includes some extreme events like hurricanes. The Burman approach is designed to handle these unusual observations better and still extract meaningful seasonal patterns.

The Burman approach:

  1. Incorporates additional techniques to handle outliers and structural breaks in the data

  2. Often uses modified filter designs that are less sensitive to unusual observations

  3. May include pre-treatment of the data to identify and adjust for special events

  4. Focuses on practical seasonal adjustment rather than theoretical optimality

  5. Can adapt to changing seasonal patterns over time

This method is particularly valuable when:

  1. Your data contains outliers or anomalies

  2. The seasonal patterns change over time (evolving seasonality)

  3. You're working with real-world messy data rather than idealized time series

  4. Practical results are more important than theoretical purity

In machine learning applications, the Burman approach can provide more reliable seasonal adjustments for real-world data, which is especially important when building models that need to be robust against outliers or when analyzing data from volatile environments where patterns may not be perfectly stable.

Canonical = 1

Uses the canonical SEATS decomposition approach, which enforces specific constraints on the components.

For Beginners: The Canonical SEATS approach is a variation that imposes additional mathematical constraints to ensure the decomposition has certain desirable properties.

Think of it like solving a puzzle with extra rules: the canonical approach adds specific requirements about how the pieces (components) should fit together, making the solution more structured.

The Canonical approach:

  1. Enforces orthogonality between components (meaning the different components don't "overlap" statistically)

  2. Minimizes the variance of the irregular component (making the trend and seasonal components explain as much of the data as possible)

  3. Produces a unique decomposition (there's only one "right answer" given the constraints)

  4. Often results in smoother trend components

  5. Makes stronger assumptions about the statistical properties of the components

This method is particularly useful when:

  1. You need components that are statistically independent from each other

  2. You want to maximize the explanatory power of your trend and seasonal components

  3. You're using the decomposition results as inputs to other statistical models

  4. You need a decomposition with well-defined mathematical properties

In machine learning contexts, the Canonical approach can provide cleaner input features for predictive models, as the separation between components is more distinct, potentially leading to better model performance when the components are used as features.

Standard = 0

Uses the standard SEATS algorithm for time series decomposition.

For Beginners: The Standard SEATS algorithm is the original implementation of the SEATS methodology, developed by the Bank of Spain and widely used in official statistics.

The Standard approach:

  1. First fits an ARIMA model to your time series data (this is a statistical model that captures patterns in the data)

  2. Then uses signal extraction techniques based on this model to separate the series into components

  3. Works in the frequency domain (which means it analyzes the data in terms of cycles and frequencies)

  4. Ensures that the components add up exactly to the original series

  5. Produces components with well-defined statistical properties

This method is particularly good at:

  1. Handling complex seasonal patterns

  2. Providing theoretically sound decompositions

  3. Working with economic and financial time series

  4. Producing results that are consistent with economic theory

In machine learning applications, the Standard SEATS algorithm provides reliable seasonal adjustments that can be used as pre-processing steps before training forecasting models, or for creating features that capture the underlying trend in the data.

Remarks

For Beginners: SEATS (Seasonal Extraction in ARIMA Time Series) is a method used to break down time series data into different components, making it easier to understand patterns and make predictions.

Think of time series data as a recording of values over time, like daily temperature readings, monthly sales figures, or quarterly economic indicators. This data often contains several patterns mixed together:

  1. Trend: The long-term direction (going up, down, or staying flat)
  2. Seasonal patterns: Regular fluctuations that repeat at fixed intervals (like higher sales during holidays)
  3. Cyclical patterns: Longer-term ups and downs (like business cycles)
  4. Irregular components: Random fluctuations that don't follow any pattern

SEATS helps separate these components by:

  1. Modeling the time series using ARIMA (AutoRegressive Integrated Moving Average) methods
  2. Identifying and extracting the seasonal patterns
  3. Separating the trend from the irregular components

Why is SEATS important in AI and machine learning?

  1. Improved Forecasting: By understanding each component separately, predictions become more accurate

  2. Pattern Recognition: Helps AI systems identify meaningful patterns versus random noise

  3. Anomaly Detection: Makes it easier to spot unusual events that don't fit established patterns

  4. Feature Engineering: Creates useful features for machine learning models from time series data

  5. Seasonal Adjustment: Allows for fair comparisons between different time periods by removing seasonal effects

This enum specifies which specific algorithm variant to use for SEATS decomposition, as different methods have different characteristics and may be more suitable for certain types of time series data.

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