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) 👇🏼
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
Acaba de ser publicado o artigo "Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps" no periódico international Chaos, Solitons, and Fractals. Esse é um trabalho de autoria de Omid Orang, minha e de Frederico Gadelha e apresenta de um novo método baseado em redes neurais aleatorizadas (Reservoir Computing) e conjuntos fuzzy para previsão multivariada de séries temporais.
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.
A nice survey paper (led by @bendfulcher) about ways to measure similarity/distance between a pair of multivariate #TimeSeries. It explains an organising structure around the different bits of literature that compares time series.
(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.
(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.
🤯 Holy sh*t, #sympy is such a crazy powerful :python: #Python package, look how much you can get out of a single one-line expression and some fancy decorators and wrappers.
(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.
Yesterday, Amazon released a new open-source project, Chronos - a family of pre-trained time series forecasting models based on language model architectures.
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!
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.
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 🚀.
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) 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!
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. 🧵👇🏼
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
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