Demonstrates fitting a non-linear temperature model to hourly dry bulb temperatures recorded in the New England region. The temperature series is modeled as a sum of two compoments, a
The Natural Gas Price model, Temperature model and Electricity Price hybrid model are jointly simulated to create market scenarios. Then, given a set of plant parameters and constraints a
When the arrays are too large, computing the entire array may not fit entirely into memory. ipdm is smart enough to break the problem up to accomplish the task anyway. In this example, the
Visualize dissimilarity data using non-classical forms of multidimensional scaling (MDS).
Use Principal Components Analysis (PCA) to fit a linear regression. PCA minimizes the perpendicular distances from the data to the fitted model. This is the linear case of what is known as
Visualize multivariate data using various statistical plots. Many statistical analyses involve only two variables: a predictor variable and a response variable. Such data are easy to
Perform "classical" multidimensional scaling, using the cmdscale function in the Statistics and Machine Learning Toolbox™. Classical multidimensional scaling, also known as
Select features for classifying high-dimensional data. More specifically, it shows how to perform sequential feature selection, which is one of the most popular feature selection