Reference health
Ensemble Based Predictive Analytics for Demand Forecasting in Multi-Channel Retailing
62/62 checkable references clean · checked 2026-08-28
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
25 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 62 checked references that resolve
resolves10.1016/j.jom.2007.01.003
Antecedents of supply chain visibility in retail supply chains: A resource‐based theory perspective
resolves10.1016/j.neucom.2019.05.099
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
resolves10.1016/j.ejor.2006.12.004
Application of machine learning techniques for supply chain demand forecasting
resolves10.1016/j.ejor.2004.02.012
Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand
resolves10.1016/j.cie.2023.109670
Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study
resolves10.1016/j.jretconser.2016.03.008
Demand forecasting based on natural computing approaches applied to the foodstuff retail segment
resolves10.1016/j.egyr.2022.11.038
Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models
resolves10.1016/j.energy.2018.09.157
Modeling and forecasting hourly electricity demand by SARIMAX with interactions
resolves10.1080/00036846.2025.2452538
Reducing product waste within the retail industry: a post-COVID-19 era study on enhancing demand prediction with hybrid prediction models
resolves10.1016/j.ijforecast.2019.04.016
The value added by machine learning approaches in forecasting
resolves10.1109/TPWRS.2006.883666
Monthly Electric Energy Demand Forecasting Based on Trend Extraction
resolves10.1016/j.neunet.2005.06.042
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
resolves10.1016/j.engappai.2019.08.018
Hybrid structures in time series modeling and forecasting: A review
resolves10.1016/S0360-8352(02)00036-0
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
resolves10.1016/j.ijforecast.2020.02.005
Daily retail demand forecasting using machine learning with emphasis on calendric special days
resolves10.1080/00207543.2020.1844332
Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand
resolves10.1016/j.ijpe.2008.08.008
Demand forecasting errors in industrial context: Measurement and impacts
resolves10.1155/2019/6434578
Mood Detection from Physical and Neurophysical Data Using Deep Learning Models
resolves10.1016/j.jclepro.2020.123285
Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance
resolves10.1016/j.dss.2012.12.008
A demand forecast model using a combination of surrogate data analysis and optimal neural network approach
resolves10.1016/j.neucom.2019.01.078
Bidirectional LSTM with attention mechanism and convolutional layer for text classification
resolves10.1016/j.dss.2018.08.010
Exploring the use of deep neural networks for sales forecasting in fashion retail
resolves10.1016/j.eswa.2023.121747
Improving flight delays prediction by developing attention-based bidirectional LSTM network
resolves10.1016/j.procir.2022.05.119
Review and analysis of artificial intelligence methods for demand forecasting in supply chain management
resolves10.1007/s00607-024-01320-y
A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering
resolves10.1080/00207543.2023.2231098
Sales forecasting of a food and beverage company using deep clustering frameworks
resolves10.1016/j.jksuci.2023.01.014
A comprehensive review on ensemble deep learning: Opportunities and challenges
resolves10.1016/j.procs.2021.01.127
Machine Learning and Statistics: A Study for assessing innovative Demand Forecasting Models
resolves10.1016/j.ejor.2006.03.047
Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches
resolves10.1016/j.ins.2019.01.076
Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model
resolves10.1080/00207543.2020.1735666
Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail
resolves10.1016/j.rcim.2014.12.015
Performance of state space and ARIMA models for consumer retail sales forecasting
resolves10.1016/j.jretai.2022.02.007
Online and offline retailing: What we know and directions for future research
resolves10.1007/s10479-019-03148-8
Demand forecasting in retail operations for fashionable products: methods, practices, and real case study
resolves10.1016/j.eswa.2024.126200
Enhancing deep learning for demand forecasting to address large data gaps
resolves10.1007/s12351-020-00605-2
Comparison of statistical and machine learning methods for daily SKU demand forecasting
resolves10.1016/j.ijforecast.2023.02.005
Demand forecasting for fashion products: A systematic review
resolves10.1016/j.sca.2023.100026
A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality
resolves10.1016/j.jom.2016.05.004
Demand forecasting and order planning for humanitarian logistics: An empirical assessment
resolves10.1016/j.jom.2014.09.004
Developing supplier integration capabilities for sustainable competitive advantage: A dynamic capabilities approach
resolves10.1016/j.ijhm.2023.103455
Application of machine learning to cluster hotel booking curves for hotel demand forecasting
resolves10.1080/08874417.2023.2240753
Retail Demand Forecasting Using Spatial-Temporal Gradient Boosting Methods
resolves10.1016/j.ejor.2023.10.039
Simplifying tree-based methods for retail sales forecasting with explanatory variables
resolves10.1016/j.eswa.2023.122333
NOA-LSTM: An efficient LSTM cell architecture for time series forecasting
resolves10.1016/j.jretconser.2010.03.019
Demand forecast information sharing in the competitive online and traditional retailers
resolves10.1162/neco_a_01199
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
resolves10.1007/s11192-020-03421-9
Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models
The 25 references without a DOI — listed, not checked
no DOI — not checkedDisaggregated retail forecasting: A gradient boosting approach
no DOI — not checkedAccurate water quality prediction with attentionbased bidirectional LSTM and encoder-decoder
no DOI — not checkedForecasting sales in the supply chain: Consumer analytics in the big data era
no DOI — not checkedXgboost: A scalable tree boosting system
no DOI — not checkedref12
no DOI — not checkedAttention-based models for speech recognition
no DOI — not checkedA novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting
no DOI — not checkedAdversarial self-attentive time-variant neural networks for multi-step time series forecasting
no DOI — not checkedShort-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation
no DOI — not checkedTime series forecast modeling of vulnerabilities in the android operating system using ARIMA and deep learning methods
no DOI — not checkedForecasting inpatient admissions in district hospitals: a hybrid model approach
no DOI — not checkedForecasting two aspects of climate change (part of Forecasting: theory and practice)
no DOI — not checkedDemand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet
no DOI — not checkedA hybrid system based on ensemble learning to model residuals for time series forecasting
no DOI — not checkedref43
no DOI — not checkedAn attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification
no DOI — not checkedThe accuracy of non-traditional versus traditional methods of forecasting lumpy demand
no DOI — not checkedref61
no DOI — not checkedref68
no DOI — not checkedOptimized multi-anchor space-aware temporal convolutional neural network for automobile sales prediction
no DOI — not checkedref73
no DOI — not checkedAttention is all you need
no DOI — not checkedEnhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches
no DOI — not checkedAttention-based bidirectional long short-term memory networks for relation classification
no DOI — not checkedref87
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-28 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.5260389"><img src="https://citestamp.com/citestamped/10.2139/ssrn.5260389/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.5260389)