Mov-Avg: Codeless time series analysis using moving averages

TL;DR

Mov-Avg is a user-friendly Python package that simplifies time series analysis by providing easy-to-use moving average indicators for trend detection and forecasting across various fields.

Contribution

It introduces a codeless, versatile Python library for applying moving averages, making time series analysis accessible without extensive programming skills.

Findings

Supports Simple, Weighted, and Exponential Moving Averages

Applicable across diverse research fields

Enhances understanding of data trends over time

Abstract

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.