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In our highly globalized and competitive business landscape, the survival and advancement of companies depend not only on the accurate prediction of market demand but also on their ability to respond swiftly to market fluctuations, particularly in an era of rapid technological advancement in artificial intelligence (AI) and big data (Angelopoulos et al., 2019). AI virtual assistants are increasingly becoming a vital force for enhancing internal collaboration and boosting productivity within enterprises.
Although the synergy of deep learning and business intelligence has opened new avenues for forecasting market demand, practical integration of these technologies into corporate collaboration environments presents notable challenges (Trakadas et al., 2020). Firms encounter specific obstacles when applying deep learning in real-world business processes, especially when it comes to handling seasonal and trend patterns, optimizing algorithms, and selecting hyperparameters—issues that warrant thorough research (H. Zhang et al., 2023).
To address these research gaps, this article introduces a novel research topic focused on delving into the best practices for deploying AI virtual assistants in internal corporate collaboration environments, and their impact on enhancing production efficiency. Specifically, our attention centers on constructing intelligent virtual assistants employing a blend of the LSTNet-Prophet model. By exploring the proficiency of long short-term memory (LSTM) technology in capturing the long-term dependencies of time series data and the adeptness of the Prophet model in managing trend and seasonality, we aim to cultivate an AI virtual assistant that can foresee market demand with impressive accuracy. Consequently, this should aid businesses in making more informed and nimble decisions and consultations.
Through this innovative approach, we intend to validate the practicality of intelligent assistants in authentic business contexts and unearth their potential role in fostering intra-enterprise collaboration and amplifying productivity. By presenting the core issues, the challenges encountered in real application scenarios, and the opportunities they present, we hope to offer readers clear insight into the direction and contributions of our research. We appreciate your guidance and will ensure that future submissions thoroughly consider and meet the expectations of reviewers, achieving greater comprehensibility and depth in our work.
In the literature exploring the fields of market demand forecasting and enterprise efficiency improvement, we note that existing research mainly tends to adopt classic forecasting methods. Especially when dealing with individual differences and dynamic environmental changes, researchers often combine traditional statistical models with advanced machine learning techniques (Min et al., 2019). These methods include artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory (LSTM). These methods are favored for their ability to identify and automatically extract complex patterns in market trends, and they have made significant contributions to improving the quality of corporate decision-making and prediction accuracy.
RNN is a powerful sequence data processing model whose basic principle is to capture temporal dependencies in data using recurrent connections between neurons (Pagliari et al., 2020). RNN has made breakthrough progress in the fields of speech recognition and natural language processing (X. Gao et al., 2021). However, traditional RNN is prone to gradient disappearance or gradient explosion problems when processing long sequences, which limits its application in long-term sequence prediction, such as market demand forecasting (Liang et al., 2022). Nonetheless, RNN has gradually shown its potential in areas such as financial market analysis and sales trend prediction through variants and improvements, such as gated recurrent units (GRU).