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You are here		www.ethanrosenthal.com Time Series for scikit-learn People (Part II): Autoregressive Forecasting Pipelines \| Ethan Rosenthal
\|	\|	dfm.io Autocorrelation time estimation \| Dan Foreman-Mackey [AI summary] This document provides a comprehensive guide to estimating autocorrelation times in Markov Chain Monte Carlo (MCMC) simulations. It begins by explaining the importance of autocorrelation in MCMC and how it affects the effective sample size. The text then introduces several methods for estimating autocorrelation times, including the Goodman & Weare (2010) method and a newer algorithm developed by the author (DFM 2017). The document also discusses the limitations of these methods with short chains and introduces a maximum likelihood approach using the celerite library to fit an autocorrelation model. Finally, it concludes with recommendations for choosing appropriate chain lengths based on the estimated autocorrelation times.	4.7 parsecs Travel
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\|	\|	phdinds-aim.github.io Chapter 1: AutoRegressive Integrated Moving Average (ARIMA) - Time Series Analysis Handbook	2.1 parsecs Travel
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\|	\|	sebastianraschka.com Predictive modeling, supervised machine learning, and pattern classification I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs).	3.1 parsecs Travel
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\|	\|	blog.scottlogic.com The basics of the Layered Neural Network Recently I've been learning about Neural Networks and how they work. In this blog post I write a simple introduction in to some of the core concepts of a basic layered neural network.	15.3 parsecs Travel
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