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.     

悉尼理工大学工程和计算机系系馆 澳大利亚悉尼

<正>悉尼理工大学工程和计算机系系馆位于市区的显赫地段,旨在重新给大学城市校区注入活力,成为悉尼商务中心南端的门户。建筑本体如同一件雕塑作品,迥然和周围的传统建筑风格相异。建筑地上14层,地下4层,设有先进的演讲厅、学术办公室、会议室、学习和研究实验室、营业咖啡厅、娱乐区、停车场,并在隔壁建筑旁设计自行车停靠处。     

Continuation methods for approximate large scale object sequencing

Machine Learning - We propose a set of highly scalable algorithms for the combinatorial data analysis problem of seriating similarity matrices. Seriation consists of finding a permutation of data...