scipy

Analyzing correlations is a critical step in understanding complex data relationships. Python offers a wide range of libraries that make calculating correlations between two time series a breeze. In this tutorial, we’ll explore some of the most popular libraries for correlation analysis, including NumPy, Pandas, Scipy, Polars, CuPy, CuDF, PyTorch, and Dask. Let’s get started! Table of Contents Correlation Between Two Time Series Using NumPy Correlation Between Two Time Series Using Pandas Correlation Between Two Time Series Using Scipy Correlation Between Two Time Series Using Polars Correlation Between Two Time Series Using CuPy Correlation Between Two Time Series Using CuDF Correlation Between Two Time Series Using Dask Correlation Between Two Time Series Using PyTorch Correlation Between Two Time Series Using NumPy NumPy is the most popular Python library for numerical computing....

In this tutorial, we will explore various detrending models using two popular Python libraries - statsmodels and scipy. While there are several detrending methods, we will focus on four models: We will start with a constant model from the scipy library, which assumes that the trend of the time series is a straight horizontal line. Then we move to a model that captures a linear trend in the data. After that, we will explore a quadratic model, using the statsmodels library....