Mov-Avg: Codeless time series analysis using moving averages

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.

• The paper introduces a codeless Python tool that automates computing SMA, WMA, and EMA for streamlined time series analysis.
• It leverages a modular architecture with a user-friendly interface to visualize trends and facilitate data interpretation.
• The approach broadens access to advanced analysis in fields like finance and public health by reducing technical barriers.

Mov-Avg: Codeless Time Series Analysis Using Moving Averages

The paper "Mov-Avg: Codeless Time Series Analysis Using Moving Averages" by Paweł Weichbroth and Jakub Buczkowski introduces a Python software package aimed at simplifying time series analysis through the application of moving averages. The Mov-Avg package enables users with minimal programming knowledge to perform effective data analysis, leveraging three primary indicators—Simple Moving Average (SMA), Weighted Moving Average (WMA), and Exponential Moving Average (EMA).

Time series analysis is a critical tool across various domains, such as finance, climate science, public health, and signal processing, to name a few. The implementation of the Mov-Avg package addresses the need for accessible analytical tools that can help identify trends, patterns, and make predictions with data accumulated over time. Its codeless design democratizes access to data analysis, potentially broadening user engagement beyond traditional data science experts.

Core Features and Software Architecture

The Mov-Avg package is developed on foundational Python libraries such as Matplotlib, Mplfinance, and Pandas-datareader, combined with a Tkinter interface, enabling visualization and interaction with the processed data. Its architecture is modular, consisting of packages for indicators, data retrieval, and user-interface management. Specifically, the Indicators package facilitates the computation of SMA, WMA, and EMA, while the Scraper and View packages manage data collection and user interface functionalities respectively.

The package's approach to identifiable indicators and trends involves a sophisticated yet user-friendly interface. Users can input data via DataFrames for local analysis or symbols for stock quote retrieval, enabling broad application in financial modeling and other time series forecasting tasks. The interface displays the time series data alongside computed moving averages, providing an intuitive visual representation of trends based on user-specified parameters. This capability is instrumental in domains like finance, where moving average crossovers can signal transactional opportunities.

Illustrative Examples

The paper provides illustrative examples and visualizations demonstrating the utility of Mov-Avg. Inputs must be structured with a Date column and value columns, facilitating versatile data handling across various datasets. The paper includes examples showcasing data handling and visualization, enhancing comprehension of its operational framework.

Implications and Impact

Mov-Avg holds significant potential for both academic and practical applications. Academically, it provides a tool for streamlined empirical research, allowing researchers to focus on interpretation rather than technical implementation. Practically, its ability to handle and visualize large datasets with minimal programming knowledge opens new avenues for non-programmers to engage in data-driven decision-making.

This package's capability to integrate seamlessly with accessible data sources such as stock markets via online APIs extends its influence to professionals in finance, economics, and supply chain management. As data becomes increasingly abundant and accessible, tools like Mov-Avg that empower diverse user groups to parse and analyze temporal data efficiently are crucial.

Future Prospects and Conclusion

Looking ahead, there is scope for extending the package's capabilities by incorporating additional time series indicators and analytics techniques. This extension would enhance its applicability across broader research scenarios and industry settings.

In conclusion, the Mov-Avg library is a significant contribution to the field of time series analysis, crafted to simplify and extend the reach of moving average methodologies to a broader audience. By reducing the complexity barrier, it stands to foster more inclusive data analysis practices, empowering researchers and professionals alike to leverage historical data for informed decision-making.