(1/2) Google released a new foundation model for time series forecasting 🚀
The TimeFM (Time Series Foundation Model) is a foundation model for time series forecasting applications. This pre-trained model was developed by the Google Research team. It joins the recent trend of leveraging foundation models for time series forecasting, which includes Salesforce's Moirai and Amazon's Chronos.
Version 0.12.0 of the skforecast Python library for time series forecasting with regression models was released this week. The release includes new features, updates for existing ones, and bug fixes. 🧵👇🏼
Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼
(1/2) Moirai - Salesforce's Foundation Forecasting Model 🚀
Salesforce recently released Moirari - a new #Python 🐍 library with a foundation model for time series forecasting applications. According to the release blog - the model comes with universal forecasting capabilities and can handle multiple scenarios and different frequencies.
Yesterday, Amazon released a new open-source project, Chronos - a family of pre-trained time series forecasting models based on language model architectures.
If you are looking to learn the foundation of time series analysis and forecasting, I recommend checking the Applied Time Series Analysis course from the University of Washington. The course by Prof Eli Holmes, Prof. Eric Ward, and Prof. Mark Scheuerell focuses on the foundation of time series analysis with applications for environmental science.
(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!
(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
Radar at 12/21 1804 currently shows the storm is parked offshore from Los Angeles (dumping quite a bit there). Still raining in Oxnard. #rain#CAwx#LosAngeles#SanDiego
So, the ghost in the HRRR machine puts totals near Santa Barbara (on 12/22/23 at 9am) at less than an Inch. But at 12/22/23 at 10am then says it thinks total accumulation will be 5 inches. It's likely a glitch (the HRRR accumulation algorithm), but I do wonder what's going to be correct. #mystery#HRRR#wx#weather#forecasting
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.
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.
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) 👇🏼
I am excited to present in November at the Oredev Developer Conference in Sweden about forecasting and MLOps 🚀. In addition, I will run a workshop about forecasting methods with regression models ❤️.
The TSstudio package is one of the first #opensource projects I did. On my way to holiday break on December 2017, I was bored during a 12 hours flight and decided to package a few common #dataviz functions I love to use for time series data. A few days after, the first version was on CRAN. In the following year, I spent many hours enjoying developing additional functionalities and tools for #timeseries analysis and #forecasting
The skforecast Python 🐍 library provides ML applications for time series forecasting using different regression models from the scikit-learn library. Here is a tutorial by Joaquín Amat Rodrigo and Javier Escobar Ortiz for time series forecasting with the skforecast using XGBoost, LightGBM, Scikit-learn, and CatBoost models 🚀.