Data Science

A Different Look at Intermittent Demand Forecasting

Demand forecasting is exactly how it sounds: forecasting demand. This can take many forms but the most classic example is predicting how many products will be sold by a retailer on each particular day. This forecasting is in turn used to drive business value in various ways. The most common is better inventory management, but […]

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To Be, or Not to Be AGI, and how do I use it? That is the question

Is ChatGPT and the its family of LLMs alive? If not, will models like these someday (soon) be alive? Do we need to be afraid of them destroying our world as we know it? And regardless of whether these things are alive or not, will they be useful? Those questions are big questions right now,

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Winning the M6 Competition: Financial Forecasting Decision Category

For the last couple of years, I have spent a large portion of my life attempting to solve the unsolvable: forecasting time series to predict the future. I have personally thought myself quite clever for the various innovations I have come up with, and the thousands of users on my free and open-source AutoTS project

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Introducing the Cassandra Model

I have always been particularly impressed with the forecasting performance of the Prophet model. It has only a few parts: seasonality, holidays, any external regressors, and with those it has some of the most consistent forecasting performance around. Also, being a linear model (with Bayesian parameter estimation), Prophet offers a decomposable explainability/interpretability, i.e. Monday has

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The simple elegance of `.get_new_params()`

Reading through documentation for a programming function to find what arguments it likes can be tedious, especially since many of the possible options either don’t work together or aren’t documented. Something I have been including on most of my newer classes is a static method called get_new_params(method=’random’) which does pretty much exactly as it says,

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Comparative runtimes of GPU vs CPU showing CPU competitive with GPU in most cases

PyTorch, Tensorflow, and MXNet on GPU in the same environment and GPU vs CPU performance

It has been impossible in the past to get all three of the largest neural network architectures running in the same Python environment in such a way that they don’t conflict and so that they will also train on GPU. The reason I want all the libraries running in the same environment is that AutoTS

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