AutoTS development has been on hold for the last three months or so. Partly that is because it finally works more or less as I originally intended. In fact, it works better than I originally intended. Still, the current development feels like a failure as I failed to use it to submit any decent models […]
AutoTS: Looking Forward Read More »
Choosing a place to travel for a week or two is not usually a big challenge. With tens of thousands of awesome places to go to, it is likely that many will be equally satisfying, give or take whatever fate may throw at you. I won’t say it is easy to choose, but rather that
Digital Nomad Index – Rank Your Best Countries Read More »
This release represents several months of fairly hard work for me. It even has a pretty, if simple documentation website that you can check out here. It features quite a number of bug fixes, including several rather critical ones that fixed cross validation not actually cross validating. As always, my biggest focus was on data
AutoTS 0.2.0 Release Read More »
VPNs are hitting the mainstream with the spread of work from home, caused by coronavirus. I am personally a major fan of remote work, and am glad the virus is finally pushing conservative corporations to embrace and adapt to the technology. Stackoverflow had an interesting article on scaling their OpenVPN to embrace their masses of
Wireguard VPN – a little praise Read More »
I have a history of obsessing over predicting the future. Probably the first major time series project I had was in grad school, with a consulting project forecasting amusement park attendance that ultimately found a LSTM network to be most effective. Again at my first data science job, forecasting demand and sales and prices. Probably
AutoTS – my new time series package for Python Read More »
Time series, while not hard persay, is not as straight-forward at the classification and regression problems that are the bread and butter of data science. Dominant models haven’t changed much in decades. This scares us data scientists, we need the latest and greatest. No consistent open-source solutions (outside of R’s forecast) Commercial is expensive for
A Practical Guide to Time Series Forecasting in Python Read More »
I love R, but almost everything my team has deployed to production is written in Python. Python can be deployed almost anywhere with just a few changes, from clusters to cloud functions. It’s flexible and fast (those who mumble about how it’s slower than C++ or whatever, well, a few milliseconds of CPU time is
R versus Python: Programming Languages for Data Science Read More »
*This is really not meant for an introduction, although there is some, but rather a reference of links and reminder for people who already know some of the basics (ie me!). I have been busy researching high-dimensional forecasting, and this is part of the result. A time series is any series of numbers that occur
Time Series Forecasting – a quick reference Read More »
https://github.com/winedarksea/ACIShinyServer Even as a primarily Python Data Scientist, I have always liked R Shiny. I think it is the best way for a data scientist to deploy a web app for end users. Lots of features. Easy to use. I have deployed apps for end users both as desktop apps and to shinyapps.io. But recently
I wrote this as notes while preparing my thoughts for a student group presentation I’m scheduled to do this week. Introduction Data science is a poorly defined field with an unclear future. Simply go on Quora, Reddit, Twitter, surf the business news, the scientific journals, and so on, and you will find opinions from those
On What Data Science Is Read More »
