A5090748216- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
- Energy Load and Power Forecasting
- Intelligent Tutoring Systems and Adaptive Learning
- Neural Networks and Applications
- Online Learning and Analytics
- Monetary Policy and Economic Impact
- Financial Risk and Volatility Modeling
- Advanced Statistical Process Monitoring
- Innovative Teaching and Learning Methods
- AI-based Problem Solving and Planning
- BIM and Construction Integration
- Collaboration in agile enterprises
- Supply Chain and Inventory Management
- Insurance, Mortality, Demography, Risk Management
- Construction Project Management and Performance
- Advanced Statistical Methods and Models
- Time Series Analysis and Forecasting
- Quality and Supply Management
- Complex Systems and Time Series Analysis
- Evacuation and Crowd Dynamics
- Atmospheric and Environmental Gas Dynamics
University of Birmingham
2019-2024
Coventry University
2016-2018
Lancaster University
2010-2014
University of Canterbury
2008-2012
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles,...
In classification, regression and time series prediction alike, cross-validation is widely employed to estimate the expected accuracy of a predictive algorithm by averaging errors across mutually exclusive subsamples data. Similarly, bootstrapping aims increase validity estimating repeatedly sub-sampling data with replacement, creating overlapping samples Estimates are then used anticipate future risk in decision making, or guide model selection where multiple candidates feasible. Beyond...
Ensemble methods represent an approach to combine a set of models, each capable solving given task, but which together produce composite global model whose accuracy and robustness exceeds that the individual models. Ensembles neural networks have traditionally been applied machine learning pattern recognition more recently forecasting time series data. Several developed network ensembles ranging from taking simple average outputs complex such as bagging boosting. Which ensemble method is...
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution that they provide one-on-one online computer-based affording student modelling, adaptive pedagogical response, and performance tracking. This study provides detailed description of design development first Forecasting System, aptly coined FITS, designed to assist...
Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs forecast users, has focused on providing optimal models likelihood various accuracy metrics. However, there is evidence that this does always lead to better inventory performance, as translation between errors results linear. In study, we consider an approach parametrising by...