Saira Karim

Forecasting stock market closing prices is a critical task for business intelligence, as it provides insights for financial planning and decision-making. The complexity arises due to diverse market patterns and various external factors that influence stock prices. This study presents a robust framework that incorporates market styles, economic indicators and news sentiments to enhance prediction power. The machine learning models are trained on aggregated datasets from companies exhibiting similar market styles. Experiments using Yahoo Finance data from five major companies (Apple, Amazon, Google, Meta, and Nike) show that accurate identification of market styles by the proposed model significantly improves pattern detection, while sentiment and economic indicators further enhance prediction quality. The findings demonstrate how multi-factor modeling can strengthen business intelligence systems by providing more reliable forecasts and supporting data-driven financial strategies.

Road traffic prediction is a crucial area currently investigated under the umbrella of the intelligent transportation system. Timely and accurate traffic prediction is a challenging problem because of the diverse nature of roads, abrupt changes in speed, and the existence of dependencies between road segments. A critical component of this research is addressing the dynamic spatial and non-linear temporal dependencies in the road network. The traffic conditions in a traffic road network change continuously, and for precise predictions, time-varying spatial correlation needs to be integrated into the model. This study intends to incorporate dynamic spatial dependencies in the Graph WaveNet model by applying attention mechanisms that can compute attention scores for the self-adaptive adjacency matrix in the time domain. We compared the computation cost of our proposed model of a graph attention network with multi-head attention with Graph WaveNet for up to 60 min. Our model gave the best result for 60-min prediction with an average percentage decrease of 3.4% and 4.76% in root-mean-square error on PEMS-BAY and METR-LA datasets, respectively. However, the model training time is increased because of the added computation of attention scores.