AIMC Topic: Commerce

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Forecasting stochastic neural network based on financial empirical mode decomposition.

Neural networks : the official journal of the International Neural Network Society
In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a p...

An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment.

Computational intelligence and neuroscience
The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today's markets experience intense activity i...

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

Computational intelligence and neuroscience
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, sin...

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

PloS one
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. ...

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

Computational intelligence and neuroscience
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective fu...

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

PloS one
Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybri...

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PloS one
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market tech...

Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs.

Acta psychologica
As organizations transition toward data-driven strategies, the ability to anticipate unarticulated consumer needs has emerged as a critical frontier in strategic marketing. This study investigates how predictive analytics, when integrated with artifi...

The competition between human and AI streamers: live streaming strategies in a duopoly market considering consumer heterogeneity.

Acta psychologica
AI streamers are increasingly prevalent in e-commerce live streaming, yet their competitive impacts and the factors driving enterprise psychological decisions remain underexplored. This paper explores the decision-making process of e-commerce enterpr...

Building organisational cyber resilience: A strategic knowledge-based view of cyber security management.

Journal of business continuity & emergency planning
The concept of cyber resilience has emerged in recent years in response to the recognition that cyber security is more than just risk management. Cyber resilience is the goal of organisations, institutions and governments across the world and yet the...