Deep-learning Model Using Hybrid Adaptive Trend Estimated Series for Modelling and Forecasting Sales

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922243" target="_blank" >RIV/00216275:25410/24:39922243 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10479-022-04838-6" target="_blank" >https://link.springer.com/article/10.1007/s10479-022-04838-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-022-04838-6" target="_blank" >10.1007/s10479-022-04838-6</a>

Result language

angličtina

Original language name

Deep-learning Model Using Hybrid Adaptive Trend Estimated Series for Modelling and Forecasting Sales

Original language description

Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at the store and product levels. To capture different big data characteristics in sales forecasting data, such as seasonal and trend variations, this study develops a hybrid model combining adaptive trend estimated series (ATES) with a deep neural network model. ATES is first used to model seasonal effects and incorporate holiday, weekend, and marketing effects on sales. The deep neural network model is then proposed to model residuals by capturing complex high-level spatiotemporal features from the data. The proposed hybrid model is equipped with a feature-extraction component that automatically detects the patterns and trends in time-series, which makes the forecasting model robust against noise and time-series length. To validate the proposed hybrid model, a large volume of sales data is processed with a three-dimensional data model to effectively support business decisions at the product-specific store level. To demonstrate the effectiveness of the proposed model, a comparative analysis is performed with several state-of-the-art sales forecasting methods. Here, we show that the proposed hybrid model outperforms existing models for forecasting horizons ranging from one to 12 months.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50204 - Business and management

Project

<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Volume of the periodical

339

Issue of the periodical within the volume

1-2

Country of publishing house

US - UNITED STATES

Number of pages

32

Pages from-to

297-328

UT code for WoS article

000819683700001

EID of the result in the Scopus database

2-s2.0-85133272456