# Time-Series Modeling in MATLAB

## Course Details

This one-day course provides a comprehensive introduction to time-series modeling using MATLAB® and Econometrics Toolbox™. The course is intended for economists, analysts and other financial professionals with prior experience of MATLAB who require to develop and maintain time-series models. The course is designed to follow the standard Box-Jenkins procedure for developing time-series models.

High-level course themes include:

• Preprocessing time-series data
• Identifying long-term and seasonal trends in time-series data
• Testing data stationarity using hypothesis tests
• Creating and fitting ARIMA and GARCH time-series models to a data set
• Comparing different model fits for the same data
• Analyzing model dynamics using Monte Carlo simulations
• Forecasting data using fitted models

### Day 1 of 1

#### Preparing Data for Model Fitting

Objective: Prepare time-series data for model fitting by identifying trends and applying data transformations.

• Removing exponential trends
• Identifying polynomial and seasonal trends
• Testing for data stationarity
• Stationarizing data
• Unit-root tests

#### Model Selection and Fitting

Objective: Use diagnostic tools to select a group of suitable candidate ARIMA and GARCH models for a given time series. Identify, create and fit candidate time-series models to data.

• Computing autocorrelation and partial autocorrelation
• Selecting models using formal tests
• Selecting candidate ARIMA and GARCH models for a given data set
• Creating and fitting time series models to a data set

#### Evaluating Model Appropriateness

Objective: Compute and evaluate model diagnostics to ensure model correctness, suitability and reliability.

• Inferring model residuals
• Testing residuals for Normality
• Analyzing model diagnostics and goodness-of-fit statistics
• Evaluating significance of individual model terms
• Comparing models

#### Forecasting and Simulation

Objective: Forecast models to predict future data. Simulate sample trajectories and statistics by applying Monte Carlo simulation techniques.

• Forecasting data using fitted models
• Using in-sample forecasts to evaluate model appropriateness
• Monte Carlo model simulation
• Backtesting models

Level: Intermediate

Prerequisites:

This program has been approved by GARP and qualifies for 7 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at https://www.garp.org/cpd

Duration: 1 day