State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Determination of the Quadrupole Splitting in Bulk n-GaAs by Warm-Up Spectroscopy

Semiconductors - We found bulk n-GaAs layers grown by liquid phase epitaxy to be irregularly stressed. Deformation created by this stress causes a small but detectable quadrupole splitting of...     

A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots

Wireless Personal Communications - A relatively new area of research and development is Swarm Robotics. It is a part of the swarm intelligence field. In the proposed paper, we shall use swarm...