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A hierarchical agglomerative clustering for product sales forecasting
Ruitenbeek, R. E. van; Koole, Ger M.; Bhulai, S. - In: Decision analytics journal 8 (2023), pp. 1-12
forecasting models for time series characterized by intermittent demand and high variations in sales quantity through the … using time series aggregation as an additional regressor. To address the lack of sales coherence within predefined business … sales history to establish more coherent aggregation clusters. We conduct a comprehensive case study featuring over 3000 …
Persistent link: https://www.econbiz.de/10014520564
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The clustering stimulus on pricing techniques and sales performance in business organisations
Falebita, Oluwabunmi A.; Adesina, Temitomiwa Matthew - In: EuroMed journal of management : EMJM 4 (2022) 3, pp. 257-268
Persistent link: https://www.econbiz.de/10013326372
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Sales forecasting of a food and beverage company using deep clustering frameworks
Mitra, Rony; Saha, Priyam; Tiwari, Manoj Kumar - In: International journal of production research 62 (2024) 9, pp. 3320-3332
Persistent link: https://www.econbiz.de/10014546818
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Vacant technology forecasting using new Bayesian patent clustering
Choi, Seongyong; Jun, Sunghae - In: Technology analysis & strategic management 26 (2014) 3, pp. 241-251
Persistent link: https://www.econbiz.de/10010338521
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Empirical comparison to predictive clustering between a bio-mimetic methodology and a non connectionist approach
Cucchi, Alain; Thebault, Marinette - In: Fuzzy economic review : the review of the International … 5 (2000) 2, pp. 27-48
Persistent link: https://www.econbiz.de/10001780053
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Incorporating statistical clustering methods into mortality models to improve forecasting performances
Tsai, Cary Chi-Liang; Cheng, Echo Sihan - In: Insurance / Mathematics & economics 99 (2021), pp. 42-62
Persistent link: https://www.econbiz.de/10012649207
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Cluster and Forecasting Analysis of the Residential Market in Turkey : An Autoregressive Model-Based Fuzzy Clustering Approach
Vatansever, Metin - 2020
house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data …
Persistent link: https://www.econbiz.de/10012830430
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unFEAR : unsupervised feature extraction clustering with an application to crisis regimes classification
Chan-Lau, Jorge A.; Wang, Ran - 2020
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes...
Persistent link: https://www.econbiz.de/10012392653
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Improving many volatility forecasts using cross-dectional volatility clusters
Coretto, Pietro; Rocca, Michele la; Storti, Giuseppe - In: Journal of risk and financial management : JRFM 13 (2020) 4/64, pp. 1-23
The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured...
Persistent link: https://www.econbiz.de/10012302505
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Improving time series forecasting: an approach combining bootstrap aggregation, clusters and exponential smoothing
Dantas, Tiago Mendes; Oliveira, Fernando Luiz Cyrino - In: International journal of forecasting 34 (2018) 4, pp. 748-761
Persistent link: https://www.econbiz.de/10012031097