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pro vyhledávání: '"Shukla, Naman"'
Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at
Externí odkaz:
http://arxiv.org/abs/2407.20192
Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice b
Externí odkaz:
http://arxiv.org/abs/2111.14938
Autor:
Shukla, Naman, Yellepeddi, Kartik
We present a novel framework to learn functions that estimate decisions of sellers and buyers simultaneously in an oligopoly market for a price-sensitive product. In this setting, the aim of the seller network is to come up with a price for a given c
Externí odkaz:
http://arxiv.org/abs/2110.13303
The importance of domain knowledge in enhancing model performance and making reliable predictions in the real-world is critical. This has led to an increased focus on specific model properties for interpretability. We focus on incorporating monotonic
Externí odkaz:
http://arxiv.org/abs/1909.10662
Multiple machine learning and prediction models are often used for the same prediction or recommendation task. In our recent work, where we develop and deploy airline ancillary pricing models in an online setting, we found that among multiple pricing
Externí odkaz:
http://arxiv.org/abs/1905.08874
Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper descri
Externí odkaz:
http://arxiv.org/abs/1902.02236
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Akademický článek
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Publikováno v:
In Hybrid Perovskite Composite Materials 2021:375-412
Publikováno v:
Journal of Ravishankar University, Part-B Science; 2022, Vol. 35 Issue 2, p1-7, 7p