The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...

Recurrence analysis has emerged as a pivotal framework for understanding the intricate behaviour of complex dynamical systems. By constructing visual representations known as recurrence plots, ...

Artificial intelligence (AI) technologies are currently revolutionizing industries and enabling automation on a scale we've never seen before. Of course, none of this is possible without data. These ...

Bayesian analysis offers a robust framework for deciphering the intricate dynamics of time series data. By treating unknown parameters as random variables, this approach incorporates prior information ...

Python. Here are some advanced things I like to do with pandas DataFrames to take my analysis to the next level. Change the ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

We develop a new statistical method for estimating functional connectivity between neurophysiological signals represented by a multivariate time series. We use partial coherence as the measure of ...

The Basics of Returns-Based Style Analysis Fetching Data for Style Analysis Case Study: Looking for Investment Style Drift Unlock More Code Snippets for Rigorous Fund Evaluation Investors choose funds ...

1. Difference Equations -- 2. Lag Operators -- 3. Stationary ARMA Processes -- 4. Forecasting -- 5. Maximum Likelihood Estimation -- 6. Spectral Analysis -- 7. Asymptotic Distribution Theory -- 8.