This is the project that I wasted many months of my life on. I say “waste” half-jokingly —I wanted to write an academic paper, but I made some fundamental errors — or just didn’t think hard enough — about certain aspects of the matter. Anyway, here’s a mini report on…

I haven’t written anything here in so long, it seemed too daunting to get back into something technical. Instead, I will state a problem and some resources to help you solve that problem. The problem: there are many methods for analyzing time series — and some use deep learning, and…

There are more cool time series libraries for Python than you can shake a stick at. You might have heard of some of them: sktime tslearn tsfresh prophet pyts Each of these libraries has different methods for dealing with the various time series learning tasks — regression, classification and forecasting…

How many dates should you go on before you settle down? The classic “secretary” problem has many names and guises but it’s essentially a mathematical decision theory problem that goes like this: imagine you are interviewing a pool of candidates, and you want to find the best one, but you…

ROCKET vs. Time Series Forest vs. Temporal Convolutional Networks vs. XGBoost — If you dabble in stock trading, as I do, you might wonder how you can tell how the stock is going to do by the time of the closing bell — is it going to close above where it started, or not? There are intraday patterns, surely — people always…

You’ve probably heard of the Poisson distribution, a probability distribution often used for modeling counts, that is, positive integer values. Imagine you’re modeling “events”, like the number of customers that walk into a store, or birds that land in a tree in a given hour. That’s what the Poisson is…

Forecasting Hourly Bike Rentals in Seoul with pycaret, keras, and Tensorflow Probability — Data: The Seoul Bike Sharing Data Set from UCI Machine Learning Repository. The target is Rented Bike Count. That means this is count data, which is positive integer values. The predictors/features are mostly weather-related, e.g. sunshine, rain, wind, visibility, as well as temporal features such as the hour of the…

(for Heart Failure Survival Prediction) — The original paper is as follows: Chicco, D., Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5 This data set has 12 features and you can download it from the UCI…

Well, it wasn’t a complete waste, you live and learn. But I spent a lot of time on the project and wanted to write it up as an academic paper — which means you have to consider what added value your paper is offering to the world, and I realized…