statsmodels

Time series data can be a valuable tool for predicting trends and making informed business decisions. However, it can be difficult to analyze due to seasonal patterns and other fluctuations that can obscure underlying trends. That’s where deseasonalizing comes in, allowing you to isolate trends and make more accurate predictions. In this tutorial, we’ll explore two different approaches to deseasonalize time series data in Python: additive models and multiplicative models....

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....

In today’s article, we’re going to explore the ins and outs of training a Holt-Winters model for forecasting multiple time series in Python. Holt-Winters is a very popular forecasting algorithm that can capture seasonality and trends in time series data through exponential smoothing. I’ll use StatsForecast, a scalable and easy-to-use Python library that can help you train a Holt-Winters model quickly and efficiently. You don’t need to be a programming wizard to get started with this library, and it can save you hours of coding time....