# Data Preprocessing

## Apps

Econometric Modeler | Analyze and model econometric time series |

## Classes

`LagOp` | Create lag operator polynomial |

## Functions

## Examples and How To

**Prepare Time Series Data for Econometric Modeler App**

Prepare time series data at the MATLAB^{®} command line, and then import the set into Econometric Modeler.

**Import Time Series Data into Econometric Modeler App**

Import time series data from the MATLAB Workspace or a MAT-file into Econometric Modeler.

**Plot Time Series Data Using Econometric Modeler App**

Interactively plot univariate and multivariate time series data, then interpret and interact with the plots.

**Transform Time Series Using Econometric Modeler App**

Transform time series data interactively.

Take a nonseasonal difference of a time series.

**Nonseasonal and Seasonal Differencing**

Apply both nonseasonal and seasonal differencing using lag operator polynomial objects.

**Moving Average Trend Estimation**

Estimate long-term trend using a symmetric moving average function.

**Seasonal Adjustment Using a Stable Seasonal Filter**

Deseasonalize a time series using a stable seasonal filter.

**Seasonal Adjustment Using S(n,m) Seasonal Filters**

Apply seasonal filters to deseasonalize a time series.

Estimate nonseasonal and seasonal trend components using parametric models.

**Using the Hodrick-Prescott Filter to Reproduce Their Original Result**

Use the Hodrick-Prescott filter to decompose a time series.

**Specify Lag Operator Polynomials**

Create lag operator polynomial objects.

## Concepts

Understand model-selection techniques and Econometrics Toolbox™ features.

**Econometric Modeler App Overview**

The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.

**Stochastic Process Characteristics**

Understand the definition, forms, and properties of stochastic processes.

Determine which data transformations are appropriate for your problem.

**Trend-Stationary vs. Difference-Stationary Processes**

Determine the characteristics of nonstationary processes.

Learn about splitting time series into deterministic trend, seasonal, and irregular components.

Some time series are decomposable into various trend components. To estimate a trend component without making parametric assumptions, you can consider using a filter.

You can use a seasonal filter (moving average) to estimate the seasonal component of a time series.

Seasonal adjustment is the process of removing a nuisance periodic component. The result of a seasonal adjustment is a deseasonalized time series.

The Hodrick-Prescott (HP) filter is a specialized filter for trend and business cycle estimation (no seasonal component).

**Time Base Partitions for ARIMA Model Estimation**

When you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample.