Posted by Jason Lillywhite
Some time-varying data we use for our models is in the form of averages for time periods that are longer than the simulation time step. This can present some challenges if your model is sensitive to large jumps in the data as the simulation walks through time. For example, monthly average values in a daily simulation model will output values that are constant for all the days of each month and jump in between them. If you feel like this is not sufficient for your modeling needs, you can use linear or spline interpolation to transition smoothly between months. In this blog post, I walk through some examples that use spline interpolation to help you see how it might be of use to you and your applications.
Some time-varying data we use for our models is in the form of averages for time periods that are longer than the simulation time step. This can present some challenges if your model is sensitive to large jumps in the data as the simulation walks through time. For example, monthly average values in a daily simulation model will output values that are constant for all the days of each month and jump in between them. If you feel like this is not sufficient for your modeling needs, you can use linear or spline interpolation to transition smoothly between months. In this blog post, I walk through some examples that use spline interpolation to help you see how it might be of use to you and your applications.
