Willie Wheeler's personal blog. Mostly tech.
These are some brief notes on Poisson processes, along with related processes and distributions.
Yesterday I wrote about how to do polynomial regression in R, and noted that it's really a form of linear regression.
This post shows how to do nonlinear regression in R using the
minpack.lm package. See
A better 'nls' (?)
At first glance, polynomial fits would appear to involve nonlinear regression. In fact, polynomial fits are just linear fits involving predictors of the form x1, x2, …, xd.
First, let's generate a data set:
Playing with the quandmod package in R. It's been around for a while but for me it's shiny and new.
library("quantmod") getSymbols("EXPE", src="google")
candleChart(tail(EXPE, 200), multi.col
While Java/Fortran integration isn't something I expect to have to do very often, I recently needed to do this, and so I figured I'd write it up in case somebody else finds it helpful.
Also, please see Calling FORTRAN and C from Java for additional...
This post describes a way to model the midpoint of a time series involving seasonal and trend components. We'll take a high-level look at an algorithm called STL, which stands for "Seasonal-Trend decomposition using Loess", and how to apply it to anomaly...
These are just some simple Python turtle graphics programs for my kids to goof around with. I may add more programs to this post over time.
import turtle colors = ["red", "yellow", "green", "blue"] numColors = len(colors) turtle.
When monitoring bookings, one common approach is to use historical levels as a baseline, and then alert if the current level is x% lower than the historical, where x is a static value that tries to strike a balance between capturing as many problems...
This is really just a set of personal notes for working with time series in R. But if it's useful to you, great.
I'm using the xts package for time series data.
For now I'm just going to do the simple stuff (summary stats, plots, moving averages...
I've been thinking of using a rules engine to help diagnose unexpected bookings drops. Rules are a good way to simplify reasoning about complex domains, and there are lots of different things that can lead to bookings drops. Examples include: