If you need to brush the dust off your way of using a db query language, then try #PromQL. It has some similarities to things you know but also is handled differently. #prometheus#timeseries
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. 🧵👇🏼
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 🚀.
Introduction to forecasting with ARIMA and seasonal ARIMA with Python 🚀👇🏼
This short tutorial by Joaquín Amat Rodrigo and Javier Escobar Ortiz provides an introduction to forecasting with ARIMA models using Python 🐍. This includes using different flavors of ARIMA methods from the statsmodels, pmdarima, skforecast, and statsForecast libraries.
(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.
(2/2) The course covers the following topics:
✅ Time series decomposition
✅ Covariance and correlation
✅ Autoregressive (AR) models
✅ Moving average (MA) models
✅ Forecasting with ARIMA models
✅ Univariate state-space models
✅ Dynamic linear models
✅ Hidden Markov models
Soo been obsessing over #math matrix & #graph theory
Thinking options for most efficient #tech application using concept of a merkle tree for a matrix; still requiring efficient merkle #matrix verification (tree=type of graph)
Saw merkle field= #hash each layer, merging into node
(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 🧵👇🏼
(2/3) The TLDR is when you need to model a short time series (less than one seasonal cycle) and have some knowledge or assumption about the expected behavior of the series - either from a similar series (i.e., similar products or geos) you can translate those assumptions to the model's prior distributions and use it to build a forecasting model.
(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.
(2/2) If you have some prior knowledge about the series (e.g., learning from similar products or goes, etc.), you should consider using the Bayesian approach.
The article below by Tim Radtke provides an example of how to incorporate prior assumptions into a time series forecasting model when having insufficient data.
Wrangling dates in R got you pulling your hair? ⏱️ Time travel to mastery with these 3 powerful tools:
Base R's seq.Date: Your daily/weekly/monthly hero.
lubridate's seq: Filter magic for specific weekdays. Analyze those Tuesdays!
timetk's tk_make_timeseries: Define complex sequences in a simple table. Easy time travel!
Drowning in daily data? Conquer weekly analysis with R's strftime() magic! Extract ISO week numbers & group your data like a pro. Ready to level up? Explore "U" for Sunday starts & packages for more grouping power. Challenge: calc weekly averages, peak sales, etc. Share your data wrangling wins in the comments!
Master date manipulation in R with two simple methods: 1) Use ifelse() to create an indicator column, and 2) Utilize subsetting to filter data based on date range. Essential for various data tasks. Try it out and enhance your R skills!
🚀 Dive into the world of data exploration with R! 📊 Uncover the earliest date lurking within your dataset using the power of R. With just a few lines of code, you can conquer this challenge and gain valuable insights into your data.
(2/3) The talk focuses on methods to identify patterns in time series using cluster analysis inspired by Prof Rob Hyndman's paper about Feature-based time series analysis 👇🏼
Seminar 🔗: https://robjhyndman.com/seminars/fbtsa-ssc/