Applying Python’s Time Series Forecasting Method in Microsoft Excel – Integration as a Business Process Supporting Tool for Small Enterprises

Jolanta Litwin

a:1:{s:5:"en_US";s:50:"Politechnika Rzeszowska im. Ignacego Łukasiewicza";}

Marcin Olech

Anna Szymusik


The paper describes the current state of research, where integration of Microsoft Excel and Python interpreter, gives the business user the right tool to solve chosen business process analysis problems like: forecasting, classification or clustering. The integration is done by using Visual Basic for Application (VBA), as well as XLWings Python’s library. Both mechanisms serve as an interfaces between MS Excel and Python to allow the data exchange between each other. Creating the suitable Graphical User Interface (GUI) in Microsoft Excel, gives the business user opportunity to select specific data analysis method available in Python’s environment and set its parameters, without Python’s programming. Running the method by Python’s interpreter can bring the results, which are hard or even impossible to obtain by using Microsoft Excel only. However, the data analysis methods stored in the Python’s script, which are available to the business user, as well as VBA source code, must be designed and implemented by the data scientist. Sample, basic integration between Microsoft Excel and Python’s interpreter is presented in the paper. To present value-added of the proposed software solution, simple case study according to time series forecasting problem is described, where forecasting errors of different methods available in the Microsoft Excel and Python are presented and discussed. The paper ends with conclusions according to the results of the current researches and suggested directions of further research.


time series forecasting, python integration, excel integration

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Cited by

Litwin, J., Olech, M., & Szymusik, A. (2021). Applying Python’s Time Series Forecasting Method in Microsoft Excel – Integration as a Business Process Supporting Tool for Small Enterprises. Technical Sciences, 24(1), 115–133.

Jolanta Litwin 
a:1:{s:5:"en_US";s:50:"Politechnika Rzeszowska im. Ignacego Łukasiewicza";}
Marcin Olech 

Anna Szymusik 


Copyright (c) 2021 Jolanta Litwin, Marcin Olech, Anna Szymusik

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