Time-Series Modeling in MATLAB

Day 1 of 1

Preparing Data for Model Fitting

Objective:由iden准备模型拟合时间序列数据tifying 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
• 蒙特卡罗模型模拟
• Backtesting models