Before any of the sophisticated decomposition algorithms running inside today's statistical software, there was a Harvard economist with a pencil and a theory about how economic time series work. Warren M. Persons published his method in 1919, in the very first issue of the Review of Economics and Statistics a journal he edited at Harvard... Continue Reading →
Wheels, edges, and figuring out what actually moves Day 5 was less about reps and more about understanding two things: edges and wheel adjustment. What Are Edges? Every roller skate has two edges: the inside edge (toward your big toe, closest to the center of your body) and the outside edge (toward your pinky toe,... Continue Reading →
When we look at a time series, the raw data often hides the underlying pattern. Short-term fluctuations make it hard to see whether the series is actually rising, falling, or simply noisy. A moving average is one of the simplest tools we have to reveal that structure. By averaging nearby observations, it smooths out random... Continue Reading →
Decomposing a time series into trend, seasonality, and remainder components is one of the most useful tools in forecasting. But there is a step that often comes before decomposition, and it is easy to overlook: cleaning up the series so that the patterns in it reflect what you actually care about. This post covers four... Continue Reading →
Regression is the foundation of most time series modelling work, trend is modelled as a function of time, seasonality is captured with indicator variables, and everything is diagnosed through residuals. Before those ideas make sense in a time series context, it helps to have the basics of regression solid. This post walks through the full... Continue Reading →
First steps on the move weight shifts, V-shape drills, and the 4-step stride Warming Up the Right Way Before putting on the skates, I did 10 repetitions of a floor-to-standing drill. It sounds straightforward: get down on both knees, place one hand on the ground, put the other hand on the knee of your standing... Continue Reading →
The previous post introduced the variety of structures a time series can carry. This one gets more concrete: how do we formally break a series into its components, and what does linear regression have to do with any of it? If you're interested in learning about time seies decomposition, we'll explore the basics and how... Continue Reading →
A time series is any sequence of observations ordered in time. The defining feature is that the ordering matters; shuffle the values and you destroy information. Yesterday's observation is part of what determines today's, and that dependency is what time series analysis tries to exploit. Most introductions start with retail sales or temperature and stay... Continue Reading →
In the previous post, we covered the main visualization tools for time series: time plots, seasonal plots, subseries plots, scatterplots, and lag plots. This post goes one level deeper into two of them; lag plots and the ACF (Autocorrelation Function). because they carry more information than they first appear to, and because reading them correctly... Continue Reading →
Today was less about moving and more about preparing the body and learning how to stay stable on skates. Before even standing up, I realized how important the basics are, things like tightening the skates properly, wearing protective gear, and practicing how to fall and stand up safely. These things might sound boring, but they... Continue Reading →
