Browsing by Author "Ardekani, I"

Now showing 1 - 2 of 2

Open Access
Forecasting accuracy of Holt-Winters Exponential Smoothing: evidence from New Zealand.
(New Zealand Journal of Applied Business Research, 2020) Dassanayake, W; Ardekani, I; Jayawardena, C; Sharifzadeh, H; Gamage, N
Financial time series is volatile, dynamic, nonlinear, nonparametric, and chaotic. Accurate forecasting of stock market prices and indices is always challenging and complex endeavour in time series analysis. Accurate predictions of stock market price movements could bring benefits to different types of investors and other stakeholders to make the right trading strategies. Adopting a technical analysis perspective, this study examines the predictive power of Holt-Winters Exponential Smoothing (HWES) methodology by testing the models on the New Zealand stock market (S&P/NZX50) Index. Daily time-series data ranging from January 2009 to December 2017 are used in this study. The forecasting performance of the investigated models is evaluated using the root mean square error (RMSE], mean absolute error (MAE) and mean absolute percentage error (MAPE). Employing HWES on the undifferenced S&P/NZX50 Index (model 1) and HWES on the differenced S&P/NZX50 Index (model 2) we find that model 1 is the superior predictive algorithm for the experimental dataset. When the tested models are evaluated overtime of the sample period we find the supportive evidence to our original findings. The evaluated HWES models could be employed effectively to predict the time series of other stock markets or the same index for diverse periods (windows) if substantiate algorithm training is carried out.
Open Access
A navigation model for side-by-side robotic wheelchairs for optimizing social comfort in crossing situations
(North-Holland, 2018-02-01) Nguyen, V. T; Ardekani, I; Jayawardena, C
One challenge in designing side-by-side robotic wheelchairs is to improve the comfort of the users, caregivers and surrounding people in crowded environments. Among different scenarios that a side-by-side robotic wheelchair has to deal with, crossing pedestrians is a common situation. Yet techniques developed for tackling the problem of passing pedestrians have still failed to take into account enough factors related to human walking behavior, therefore the navigation plan is not natural. To tackle this problem, this paper proposes a novel navigation model for side-by-side robotic wheelchairs that considers the Friendly Link factor and Preferred Walking Velocity related to the comfort of wheelchair users, caregivers and pedestrians. The model is carried out based on an experimental observation and data collection. The developed model is then validated by comparing the distance errors between the moving solutions of the new model and previous methods with the real solutions of humans based on a natural walking scenario. The experimental results show that the performance of the proposed technique is significantly better than that of previous techniques.