- Cover Page
- Title Page
- Copyright Page
- Contents
- Learn About JMP
- Launch the Fit Model Platform
- Example of the Fit Model Platform
- Specify Different Model Types
- Construct Model Effects
- Fitting Personalities
- Emphasis Options for Standard Least Squares
- Model Specification Options
- Validity Checks
- Fitting Standard Least Squares Models
- Example Using Standard Least Squares
- The Standard Least Squares Report and Options
- Regression Reports
- Estimates
- Effect Screening
- Factor Profiling
- Row Diagnostics
- Save Columns
- Effect Options
- Restricted Maximum Likelihood (REML) Method
- Method of Moments Results
- Singularity Details
- Examples with Statistical Details
- Fitting Stepwise Regression Models
- Overview of Stepwise Regression
- Example Using Stepwise Regression
- The Stepwise Report
- Models with Crossed, Interaction, or Polynomial Terms
- Models with Nominal and Ordinal Terms
- Using the Make Model Command for Hierarchical Terms
- Performing Logistic Stepwise Regression
- The All Possible Models Option
- The Model Averaging Option
- Using Validation
- Fitting Multiple Response Models
- Fitting Generalized Linear Models
- Performing Logistic Regression on Nominal and Ordinal Responses
- Analyzing Screening Designs
- Performing Nonlinear Regression
- Creating Neural Networks
- Modeling Relationships With Gaussian Processes
- Fitting Dispersion Effects with the Loglinear Variance Model
- Recursively Partitioning Data
- Performing Time Series Analysis
- Performing Categorical Response Analysis
- Performing Choice Modeling
- Introduction to Choice Modeling
- Product Design Engineering
- Data for the Choice Platform
- Example: Pizza Choice
- Launch the Choice Platform and Select Data Sets
- Platform Options
- Example: Valuing Trade-offs
- One-Table Analysis
- Segmentation
- Special Data Rules
- Transforming Data
- Logistic Regression for Matched Case-Control Studies
- Correlations and Multivariate Techniques
- Clustering Data
- Analyzing Principal Components and Reducing Dimensionality
- Performing Discriminant Analysis
- Fitting Partial Least Squares Models
- Scoring Tests Using Item Response Theory
- Plotting Surfaces
- Visualizing, Optimizing, and Simulating Response Surfaces
- Comparing Model Performance
- References
- The Response Models
- The Factor Models
- The Usual Assumptions
- Key Statistical Concepts
- Leverage Plot Details
- Multivariate Details
- Power Calculations
- Inverse Prediction with Confidence Limits
- Details of Random Effects
- Index
Chapter 14
Performing Time Series Analysis
Using the Time Series Platform
The Time Series platform lets you explore, analyze, and forecast univariate time series. A time series is a set y1, y2, ... ,yN of observations taken over a series of equally-spaced time periods. The analysis begins with a plot of the points in the time series. In addition, the platform displays graphs of the autocorrelations and partial autocorrelations of the series. These indicate how and to what degree each point in the series is correlated with earlier values in the series. You can interactively add:
Variograms
a characterization of process disturbances
AR coefficients
autoregressive coefficients
Spectral Density Plots
versus period and frequency, with white noise tests.
These graphs can be used to identify the type of model appropriate for describing and predicting (forecasting) the evolution of the time series. The model types include:
ARIMA
autoregressive integrated moving-average, often called Box-Jenkins models
Seasonal ARIMA
ARIMA models with a seasonal component
Smoothing Models
several forms of exponential smoothing and Winter’s method
Transfer Function Models
for modeling with input series.
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