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Based on our record, Jekyll seems to be a lot more popular than PyPOTS. While we know about 195 links to Jekyll, we've tracked only 3 mentions of PyPOTS. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The static site generator (SSG) landscape is crowded with feature-rich but increasingly complex solutions. As I looked at and used tools like lume, 11ty, lektor, or jekyll, I found myself drowning in configuration options, plugins, and middleware. What started as a simple desire to convert Markdown content into HTML had evolved into learning complex frameworks with steep learning curves. - Source: dev.to / about 3 hours ago
If you don't want to use Jekyll as your static site generator for GitHub Pages and you want to have a custom domain for your GitHub Pages. This post is for you! - Source: dev.to / 3 months ago
Jekyll is a static site generator that transforms Markdown files into a fully functional website. Everything is generated into plain HTML, which makes it simple to deploy on platforms like GitHub Pages. - Source: dev.to / 3 months ago
Obviously, there are a dozen choices for generating static websites (efficiently and quickly), from the classic Jekyll to the new Next.js. And you are good to go with any of them as long as your confident with it. I choose 11ty because:. - Source: dev.to / 4 months ago
In your repository settings you need to turn on GitHub Pages to make it pull Jekyll content (that's the magic✨ default GitHub Pages build tool) from your GitHub repository. - Source: dev.to / 10 months ago
Absolutely my pleasure! Please pay a visit to the toolbox PyPOTS https://pypots.com if you're interested in modelling partially-observed time series (POTS). It deserves your attention ;-). Source: almost 2 years ago
If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series https://pypots.com/. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23). Source: almost 2 years ago
Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modelling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated... Source: almost 2 years ago
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