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Demand Forecasting in Python: Deep Learning Model Based on LSTM Architecture versus Statistical Models

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F21%3A10247955" target="_blank" >RIV/61989100:27510/21:10247955 - isvavai.cz</a>

  • Výsledek na webu

    <a href="http://acta.uni-obuda.hu/Kolkova_Navratil_115.pdf" target="_blank" >http://acta.uni-obuda.hu/Kolkova_Navratil_115.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.12700/APH.18.8.2021.8.7" target="_blank" >10.12700/APH.18.8.2021.8.7</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Demand Forecasting in Python: Deep Learning Model Based on LSTM Architecture versus Statistical Models

  • Popis výsledku v původním jazyce

    Demand forecasting for business practice is one of the biggest challenges of current business research. However, the discussion on the use of forecasting methods in business is still at the beginning. Forecasting methods are becoming more accurate. Accuracy is often the only criterion for forecasting. In the reality of business practice or management is also influenced by other factors such as runtime, computing demand, but also the knowledge of the manager. The goal of this article is to verify the possibilities demand forecasting using deep learning and statistical methods. Suitable methods are determined on based multi-criteria evaluation. Accuracy according to MSE and MAE, runtime and computing demand and knowledge requirements of the manager were chosen as the criteria. This study used univariate data from an e-commerce entity. It was realized 90-days and 365-days demand forecasting. Statistical methods Seasonal naive, TBATS, Facebook Prophet and SARIMA was used. These models will be compared with a deep learning model based on recurrent neural network with Long short-term memory (LSTM) layer architecture. The Python code used in all experiments and data is available on GitHub (https://github.com/mrnavrc/demand_forecasting). The results show that all selected methods surpassed the benchmark in their accuracy. However, the differences in the other criteria were large. Models based on deep learning have proven to be the worst on runtime and computing demand. Therefore, they cannot be recommended for business practice. As a best practice model has proven Prophet model developed at Facebook.

  • Název v anglickém jazyce

    Demand Forecasting in Python: Deep Learning Model Based on LSTM Architecture versus Statistical Models

  • Popis výsledku anglicky

    Demand forecasting for business practice is one of the biggest challenges of current business research. However, the discussion on the use of forecasting methods in business is still at the beginning. Forecasting methods are becoming more accurate. Accuracy is often the only criterion for forecasting. In the reality of business practice or management is also influenced by other factors such as runtime, computing demand, but also the knowledge of the manager. The goal of this article is to verify the possibilities demand forecasting using deep learning and statistical methods. Suitable methods are determined on based multi-criteria evaluation. Accuracy according to MSE and MAE, runtime and computing demand and knowledge requirements of the manager were chosen as the criteria. This study used univariate data from an e-commerce entity. It was realized 90-days and 365-days demand forecasting. Statistical methods Seasonal naive, TBATS, Facebook Prophet and SARIMA was used. These models will be compared with a deep learning model based on recurrent neural network with Long short-term memory (LSTM) layer architecture. The Python code used in all experiments and data is available on GitHub (https://github.com/mrnavrc/demand_forecasting). The results show that all selected methods surpassed the benchmark in their accuracy. However, the differences in the other criteria were large. Models based on deep learning have proven to be the worst on runtime and computing demand. Therefore, they cannot be recommended for business practice. As a best practice model has proven Prophet model developed at Facebook.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    50206 - Finance

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/GA18-13951S" target="_blank" >GA18-13951S: Nové přístupy k modelování finančních časových řad pomocí soft-computingu</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Ostatní

  • Rok uplatnění

    2021

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Acta Polytechnica Hungarica

  • ISSN

    1785-8860

  • e-ISSN

  • Svazek periodika

    18

  • Číslo periodika v rámci svazku

    8

  • Stát vydavatele periodika

    HU - Maďarsko

  • Počet stran výsledku

    19

  • Strana od-do

    123-141

  • Kód UT WoS článku

    000697928900007

  • EID výsledku v databázi Scopus

    2-s2.0-85117762084