Inventory management is essential for economic success of companies since it represents a significant part of their financial balance. Stockouts represent one of the major issues that inventory management has to deal with. In case that enough …

Spatiotemporal-based intelligent transportation systems are increasingly being integrated into various surveillance systems. To enhance the efficiency of these systems, automated forecasting was introduced to identify and penalize non-compliant …

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research …

The spatial and temporal dynamics of many real-world systems present a significant challenge to multi-variate forecasting where features of both forms, as well as their inter-dependencies, must be modeled correctly. State-of-the-art approaches …

Accurate traffic forecasting is pivotal for an efficient data-driven transportation system. The intricate nature of spatial-temporal dependencies and non-linearity present in traffic data has posed a significant challenge to the modeling of …

Natural Language Processing (NLP) refers to a computer program’s ability to understand spoken and written human language. It is utilized to do large-scale research, receive a more objective and accurate analysis, increase customer satisfaction …

How to model the spatial-temporal graph is a crucial problem for the accuracy of traffic forecasting. Existing GNN-based work mostly captures spatial dependencies by using a pre-defined graph for close nodes and a self-adaptive graph for distant …

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement …

While some methods are confined to linear embeddings and others exhibit limited robustness, high-dimensional time series factorization techniques employ scalable matrix factorization for forecasting in latent space. This paper introduces a novel …

Graph Recurrent Neural Networks (GRNN) excel in time-series prediction by modeling complicated non-linear relationships among time-series. However, most GRNN models target small datasets that only have tens of time-series or hundreds of …

Correlations between variables in complex ecosystems such as weather and financial markets lead to a great amount of dynamic and co-evolving time series data, posing a significant challenge to the current forecast methods. Discovering dynamic …

This article explores the application of machine learning techniques to improve forecasting efficiency for the stock market. Machine learning models have the potential to capture complex patterns and dependencies in stock market trends, enabling …

Due to the growing carbon footprints and the impacts of climate change, less fossil fuels are being used for transportation and energy production. The cost of creating solar photovoltaic (PV) panels has been reduced as a result of the improvements …

The water quality index (WQI) serves as a global representation of river water quality (WQ). Existing studies related to the WQI have mainly focused on two aspects: (i) a WQI point estimation using multiple WQ inputs; and (ii) a one-step-ahead WQI …

During trip planning and booking, tourists can search the web using different devices, such as personal computers (PC) or mobile devices. Search engine data can help understand the diverse aspects of tourists’ preference and search behavior.

Stock price prediction has long been a pivotal area of interest for investors, financial analysts, and researchers alike. The ability to forecast future stock prices accurately can provide substantial benefits in investment decision-making. With …