This is an exercise for my Cryptography course in university. We were asked to use LFSR to generate a pseudorandom binary sequence and check if it satisfies Golomb's postulates for randomness. I used ...

Autocorrelation is a phenomenon where the values of a variable are correlated with its previous or lagged values. This can cause problems in machine learning, especially when using time series data, ...

This project is a Python web application that allows importing data from a CSV file into a SQLite database and then performing a correlation analysis between two columns. The application is written in ...

Abstract: Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time ...

There are several ways to test for stationarity in your time series data, but one of the most popular ones is the Augmented Dickey-Fuller (ADF) test. The ADF test is based on the idea that a ...

Autocorrelation and partial autocorrelation are essential tools in time series analysis and forecasting. These concepts help assess the relationship between current observations and previous time ...

Time series data is collected sequentially, allowing for the analysis of relationships over time. Correlation quantifies the relationship between two variables, indicating how one affects the other.