(1/2) Microsoft Forecasting Framework for Finance Time Series 🚀
The finnts is an R package from Microsoft for automated forecasting for financial forecast. This includes the following features:
✅ Automated feature engineering and feature selection
✅ Training models with backtesting, scoring, and ranking
✅ Supports more than 25 models for univariate and multivariate time series
✅ Supports daily, weekly, monthly, quarterly, and yearly frequency
#RStats #DataScience #forecasting #MachineLearning
Thanks to Forecasting for Social Good for inviting me to deliver a workshop on "Forecasting with Generalised Additive Models (GAMs) in R" today! We talked about:
❓ What are GAMs?
❓ How do we fit GAMs in R?
❓ Model evaluation and forecasting
❓ When GAMs go wrong!
Posit Cloud: https://posit.cloud/content/7637971
(1/3)Modeling Short Time Series with Prior Knowledge in PyMC 🚀
Yesterday, I shared an article by Tim Radtke about forecasting insufficient time series data with a Bayesian approach using R. Here is the Python version 🧵👇🏼
(1/2) Modeling Short Time Series with Prior Knowledge
When modeling time series data, you may find yourself with insufficient data. Insufficient data in time series would typically be defined as less than one seasonal cycle. This would challenge us to understand whether some events are driven by seasonality or other reasons, such as one-time events, outliers, etc.
Weird day. We were expected to get 6-10 inches of snow starting at 2 am and lasting through this afternoon. Schools closed. City declared a snow emergency. But it just started snowing at 8:30 and it looks like it'll only be a dusting.
Hudson Valley #Weather: “This is one of the more severe changes [to a forecast] we’ve seen in recent memory. For the data to be unanimous in the result… and then to all begin shifting dramatically SE in about 12 to 18 hours, is a rare event.” #forecasting
Join me on February 21st for a Forecasting for Social Good (F4SG) workshop about forecasting using GAMs in R! 📈
Tech workers who make 6-7 figures a year destroyed the income of freelancers earning pennies and we called it progress.
It was a pleasure to present about forecasting at scale at the South Dakota State University Data Science Club meetup last month. The event recording is now available 👇🏼
Thanks to Prof Steven Ge for the invite! 🙏🏼
The Collapse of Western Civilization: A View From the Future
This work is an important title that will change how readers look at the world. Dramatizing climate change in ways traditional nonfiction cannot, this inventive, at times humorous work reasserts the importance of scientists and the work they do and reveals the self-serving interests of the so called carbon industrial complex that have turned the practice of sound science into political fodder.
Disaster early-warning systems are ‘doomed to fail’ — only collective action can plug the gaps
From floods to wildfires, and tsunamis to volcanic eruptions, early-warning systems can stop natural hazards becoming human disasters. But more joined-up thinking is urgently needed.
https://www.nature.com/articles/d41586-023-03510-8 #disaster #EarlyWarning #systems #NaturalHazards #forecasting
Working on a talk focusing on methods for analyzing and forecasting time series at scale. Having fun with Prof. Rob Hyndman's approach to analyzing multiple time series with dimension reduction for time series features with principle components 😍
Code (WIP) 🔗: https://github.com/RamiKrispin/forecasting-at-scale
I am excited to present today at the SDSU Data Science Club meetup on approaches for forecasting at scale. The event at 4 PM PST / 6 PM CDT is open to the public via Zoom, and you can register in the link below.
All the talk materials will be available here: https://github.com/RamiKrispin/forecasting-at-scale
Thanks to Prof. Steven Ge for the invite! 🙏
(1/2) Timetk for Python 🚀🚀🚀
The timetk, one of the main R packages for time series analysis and forecasting ❤️, by Matt Dancho, is now available in Python 🐍. The package provides a variety of tools for working with time series data and analyzing it. The Python version leverages pandas for processing time series data and plotly for visualization.
My R Shinylive app is now on Github Pages. It was simple and straightforward to deploy the app on Pages, a tutorial to follow...
Please be patient while loading 😝
- Easy to deploy as a website on Github Pages (and similar)
- Slow load time
- Still experimental
I spent last night to build this fun Shinylive app - Forecasting Sandbox 😎
The app provides a simple sandbox for three simple forecasting models - Linear regression, ARIMA, and Holt-Winters, and it entirely runs on the browser!
I am planning to deploy it to Github Actions and create a tutorial (WIP) 👇🏼