This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weig...

In the process of cointegration analysis, electricity consumption is chosen as the explained variable, and GDP per capita, heavy industry share, and efficiency improvement are chosen as the explanatory variables; then a cointegration model is put forward, which shows that there is a cointegration relationship between the explained variable and explanatory variables. The explained and explanator...

Stock price forecasting models with lower Mean Squared Error (MSE) are more “welcome" than those who have higher MSE. However, this “welcome" may disappear when lower MSE leads to an unexpected loss whereas higher MSE leads to a positive profit in a practical investment. The reason is that MSE aims to evaluate models in the view of “accuracy", which has nothing to do with profit. We therefore p...

The paper focuses on the forecasting risk analysis in the supply chains with the intermittent demand, which is typical for the inventory management of the “slow moving items” such as service parts or high-priced capital goods. The adopted demand model is based on the generalised beta-binomial distribution, which is capable to incorporate the additive distortions in the demand historical records...

This paper examines the predictability of real estate asset returns using a number of time series techniques. A vector autoregressive model, which incorporates financial spreads, is able to improve upon the out of sample forecasting performance of univariate time series models at a short forecasting horizon. However, as the forecasting horizon increases, the explanatory power of such models is ...

A double forecasting model based on conditional expectation is proposed through probability distribution of port cargo throughput. A compound variable, being composed of the number of cargo ships arriving at the port and the operating efficiency of handling equipment, is constructed. The port cargo throughput is the sum of all compound variables which indicated the cargo throughput each time wh...

The work reported here was undertaken to gain an understanding of the weather forecasting process in a naturalistic setting that involves task switching and moderate time pressure. In the first study, Navy weather forecasters verbalized their thoughts while they completed a standard forecasting task. In the second study, Navy weather forecasters completed a questionnaire after finishing each of...

Short term electrical load forecasting is critical in ensuring reliability and operational efficiency for electrical systems. With an influx of monitoring data and the growing technical complexity of the grid, there is a great interest and need for accurate forecasting in electricity planning. Our project uses a curated electric load dataset from Kaggle and evaluates the performance of several ...

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962–1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway’s (2001) model as opposed to Altman (1968) and Zmijewski (19...

A probability sequence is an ordered set of probability forecasts for the same event. Although single-period probabilistic forecasts and methods for evaluating them have been extensively analyzed, we are not aware of any prior work on evaluating probability sequences. This paper proposes an efficiency condition for probability sequences and shows properties of efficient forecasting systems, inc...