Class ARIMAXModelOptions<T>
Configuration options for the ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) time series forecasting model.
public class ARIMAXModelOptions<T> : TimeSeriesRegressionOptions<T>Type Parameters
TThe data type of the time series values.
- Inheritance
-
ARIMAXModelOptions<T>
- Inherited Members
Remarks
ARIMAX extends the ARIMA model by incorporating external (exogenous) variables that may influence the time series. This allows the model to account for known external factors when making predictions.
For Beginners: ARIMAX is like ARIMA (which predicts future values based on past patterns) but with an added superpower: it can also consider outside factors that might affect your data. For example, when predicting ice cream sales, ARIMA would only look at past sales patterns, but ARIMAX could also consider temperature data. Think of it as a weather forecaster who not only looks at past weather patterns but also considers upcoming events like a hurricane forming in the ocean that will likely affect the forecast.
Properties
AROrder
Gets or sets the order of the AutoRegressive (AR) component.
public int AROrder { get; set; }Property Value
- int
The AR order, defaulting to 1.
Remarks
The AR order specifies how many previous time steps are used to predict the current value. For example, AROrder=1 means the model uses only the immediately preceding value.
For Beginners: This controls how many past data points the model looks at to make predictions. With the default value of 1, the model only considers what happened in the previous time period. If you set it to 2, it would look at the last two time periods, and so on. Think of it like predicting tomorrow's sales based on today's sales (AROrder=1) versus considering both today and yesterday (AROrder=2). Higher values can capture more complex patterns but might make the model unnecessarily complicated for simple data.
DecompositionType
Gets or sets the matrix decomposition method used for solving the model's equations.
public MatrixDecompositionType DecompositionType { get; set; }Property Value
- MatrixDecompositionType
The matrix decomposition type, defaulting to Cholesky.
Remarks
Different matrix decomposition methods offer trade-offs between computational efficiency, numerical stability, and applicability to different types of matrices. Cholesky decomposition is efficient for positive definite matrices.
For Beginners: This is a technical setting that determines the mathematical method used to solve the equations in the model. The default Cholesky method is a good balance of speed and accuracy for most cases. Think of it like choosing which calculator to use - some calculators are better for certain types of math problems. Unless you have specific knowledge about the mathematical properties of your data, it's usually best to leave this at the default setting.
DifferenceOrder
Gets or sets the order of differencing (Integration).
public int DifferenceOrder { get; set; }Property Value
- int
The differencing order, defaulting to 0.
Remarks
The differencing order specifies how many times the data is differenced to achieve stationarity. Differencing helps remove trends and seasonal patterns from the data. A value of 0 means no differencing is applied.
For Beginners: This determines how many times the model subtracts consecutive values to remove trends. The default value of 0 means the model works with the raw values directly. If set to 1, the model would work with the changes between consecutive values rather than the raw values themselves. For example, instead of using temperatures like [70°F, 72°F, 75°F], it would use the differences [2°F, 3°F]. This helps the model focus on how values are changing rather than their absolute values, which is useful when data has an upward or downward trend. Think of it like focusing on how much warmer or cooler it gets each day rather than the actual temperature.
ExogenousVariables
Gets or sets the number of exogenous (external) variables to include in the model.
public int ExogenousVariables { get; set; }Property Value
- int
The number of exogenous variables, defaulting to 1.
Remarks
Exogenous variables are external factors that may influence the time series but are not influenced by it. This parameter specifies how many such variables the model should consider.
For Beginners: This sets how many outside factors the model will consider when making predictions. The default value of 1 means the model will use one external factor (like temperature when predicting ice cream sales). These are factors that affect your data but aren't affected by it. For example, when predicting retail sales, external factors might include holidays, weather, or marketing campaigns. Including these can significantly improve predictions when your data is influenced by known external conditions.
MAOrder
Gets or sets the order of the Moving Average (MA) component.
public int MAOrder { get; set; }Property Value
- int
The MA order, defaulting to 1.
Remarks
The MA order specifies how many previous forecast errors are used in the model. For example, MAOrder=1 means the model incorporates the error from the previous prediction.
For Beginners: This controls how many past prediction errors the model considers. With the default value of 1, the model adjusts based on how wrong its most recent prediction was. Think of it like a weather forecaster who not only looks at yesterday's weather but also considers how wrong their previous forecast was, learning from their mistakes. If yesterday's prediction was too high, they might adjust today's prediction downward a bit to compensate.