This paper introduces Mov-Avg, the Python software package for time series analysis that requires little computer programming experience from the user. The package allows the identification of trends, patterns, and the prediction of future events based on data collected over time. In this regard, the Mov-Avg implementation provides three indicators to apply, namely: Simple Moving Average, Weighted Moving Average and Exponential Moving Average. Due to its generic design, the Mov-Avg software package can be used in any field where the application of moving averages is valid. In general, the Mov-Avg library for time series analysis contributes to a better understanding of data-driven processes over time by taking advantage of moving averages in any way adapted to the research context.

翻译：本文介绍Mov-Avg，这是一个用于时间序列分析的Python软件包，对用户的计算机编程经验要求极低。该软件包能够基于随时间收集的数据识别趋势与模式，并预测未来事件。为此，Mov-Avg提供了三种可应用的指标，即：简单移动平均、加权移动平均和指数移动平均。由于其通用设计，Mov-Avg软件包可应用于任何适合使用移动平均的领域。总体而言，用于时间序列分析的Mov-Avg库通过以任何适应研究背景的方式利用移动平均，有助于更好地理解随时间变化的数据驱动过程。