T-Stop After trying toe stop and plow stop, I wanted to learn something that sits a bit in between, more controlled than toe stop, but less “wide” than plow. So today I worked on the T-stop. At first, it looked simple: one foot in front, the other dragging behind. In practice, it’s mostly about balance... Continue Reading →
Part 5: The Final Preparations Before Modelling The feature engineering is almost done. Four families of features now cover calendar position, lagged history, rolling averages, and exponentially weighted averages. Two housekeeping steps remain before the model can be trained: encoding the categorical columns properly and transforming the target variable. One-Hot Encoding The dataset still contains... Continue Reading →
Exponentially Weighted Mean Features The rolling mean from the previous post computes a simple average over a fixed window of past values. Every observation inside the window contributes equally, a sale from 364 days ago counts just as much as a sale from 2 days ago. The triangular window improved on this slightly by weighting... Continue Reading →
The lag features from the previous post handed the model raw historical sales values, the exact number sold on a specific day 91 or 364 days ago. That is precise, but precision is not always what you want. A single day's sales figure is noisy. It might have been a public holiday, an unusual promotion,... Continue Reading →
Plow Stop After trying the toe stop drag, I wanted to learn a stopping method that feels a bit more controlled. So today I worked on the plow stop (also known as plough stop). The plow stop starts from a position that already feels quite different from normal skating. Feet are wide apart, knees are... Continue Reading →
Part 2: Teaching the Model About the Past Lag Features Calendar features: month, day of week, and is_weekend tell the model what time it is. That is useful for capturing seasonality and weekly rhythms, but it leaves out something equally important: what actually happened in the recent past. A store that sold 90 units last... Continue Reading →
Part 1: The Problem, the Data, and the Features For the past several posts, we built forecasting models the classical way: exponential smoothing, ARIMA, SARIMA. Each of those methods works on a single time series at a time, trying to model how a variable evolves through trend, seasonality, and noise. That framing is clean, but... Continue Reading →
The previous post established what stationarity means and how to recognise when a series lacks it. To formally assess this, we also need a statistical test. The airline passenger series fails on both counts: a clear upward trend and a repeating seasonal cycle, confirmed by an ACF that barely drops across 40 lags. This post... Continue Reading →
Toe Stop Drag Today I tried the toe stop drag, which is usually one of the first stopping methods people learn on quad skates. At first, the idea sounded simple: one foot stays in front carrying most of the weight, and the other foot drags behind using the toe stop. In practice, not that simple.... Continue Reading →
The previous posts built up a set of forecasting tools; SES, Holt-Winters, ARIMA, SARIMA, and each one quietly assumed something about the series it was working with. That assumption is stationarity. This post is about making that assumption explicit: what stationarity means, how to spot it, and what happens when a series violates it. Time... Continue Reading →
