Comparison of ARIMA, ANN and Hybrid ARIMA-ANN Models for Time Series Forecasting

This paper aims to compare between Auto Regressive Integrated Moving Average (ARIMA) model, Artificial Neural Networks (ANN) and hybrid models for time series forecasting. The dataset used on this study is based on the monthly gold prices during Nov-1989 to Dec-2019. This dataset was used to train and test the predictive models. The performances were evaluated based on three metrics Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to determine the more appropriate model and evaluate models’ performance. The most important finding was that applying hybrid models can improve the forecasting accuracy over the ARIMA and ANN models. This may suggest that neither ARIMA nor ANN model captures all of patterns in the data

A New Initialisation Method for Examination Timetabling Heuristics

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Timetabling problems are widespread, but are particularly prevalent in the educational domain. When sufficiently large, these are often only effectively tackled by timetabling meta-heuristics. The effectiveness of these in turn are often largely dependant on their initialisation protocols. There are a number of different initialisation approaches used in the literature for starting examination timetabling heuristics. We present a new iterative initialisation algorithm here --- which attempts to generate high-quality and legal solutions, to feed into a heuristic optimiser. The proposed approach is empirically verified on the ITC 2007 and Yeditepe benchmark sets. It is compared to popular initialisation approaches commonly employed in exam timetabling heuristics: the largest degree, largest weighted degree, largest enrollment, and saturation degree graph-colouring approaches, and random schedule allocation. The effectiveness of these approaches are also compared via incorporation in an exemplar evolutionary algorithm. The results show that the proposed method is capable of producing feasible solutions for all instances, with better quality and diversity compared to the alternative methods. It also leads to improved optimiser performance.Saudi Arabia Cultural Burea

University Examination Timetable Optimisation: Analysis, Initialisation, and Effective Heuristic Optimisation

In higher education institutions, particularly universities, the task of scheduling examinations is a heavily constrained problem involving the allocation of exams, and corresponding enrolled students, to examination rooms over a limited number of periods. This is commonly performed by heuristic optimisers, as the task is NP-complete. The research work presented in this thesis focuses on examination timetabling with the aim of investigating three main areas: (i) initialising efficient seeded solutions for starting examination timetabling heuristics, (ii) developing a novel genetic algorithm-based examination timetabling optimiser, and (iii) analysing and comparing published works from the literature. The new iterative initialisation algorithm presented here attempts to generate legal and high-quality solutions, to feed into a heuristic optimiser. Subject to satisfying hard constraints, it schedules as many conflicting examinations as possible in the early and late periods of a timetable, whilst managing the soft constraints. The proposed initialisation strategy is empirically verified on problem instances from two different benchmark sets: ITC 2007 and Yeditepe, and compared to a number of popular initialisation approaches. The effectiveness of this approach is also evaluated via incorporation in an exemplar evolutionary algorithm. This thesis also investigates a novel genetic algorithm that incorporates new operators to avoid various types of violations that occur in the exam timetable optimisation task. It utilises the initialisation approach developed earlier in the thesis, as well as specialised operators for search. It is validated on a range of problems and is seen to produce results that place its performance amongst the state-of-the-art. The third area of investigation of this thesis is concerned with issues in the comparison between published works on examination timetabling in the literature. Such comparison is often difficult and can be misleading because results obtained differ significantly in run-times --- even when variations in computational power are accounted for. Consequently, a multi-objective comparison scheme based on (uncertain) Pareto dominance is presented and utilised with the aim of comparing published exam timetabling approaches on the Toronto benchmark sets to identify the (probabilistic) Pareto set of optimisers