It has been deemed as an effective tool of forecasting performance improvement to combine different component forecasting models. However, current nonlinear combining models are not able to meet the requirement of high forecasting accuracy in practice. To tackle this challenge, this paper constructs a hybrid, named genetic programming and least squared estimation based nonlinear combining metho...

We examine the relation between the importance for firms to meet external performance benchmarks and the role of internal forecasting and misreporting for increasing the likelihood of meeting benchmarks. Drawing on survey data from investment centers, we hypothesize and find that the importance of meeting benchmarks is positively associated with the sophistication of firms’ internal forecasting...

This study examines the forecasting performance of Adaptive Neuro Fuzzy Inference System(ANFIS) compared in comparison to statistical autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) model in forecasting of rice yield production.. To assess the effectiveness of these models, we used 9 years of time series records for rice yield data in Malaysia from 1995 ...

Implementation and Evaluation of a Mesoscale Short-range Ensemble Forecasting System over the Pacific Northwest

Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We describe the Fa system where users and applications can pose declarative forecasting queries—both onetime queries and continuous queries—and get forecasts in real-time along with accuracy estimates. Fa supports efficie...

In the late 1970s, Professor Armstrong wrote a treatise on long-range forecasting that many readers considered irreverent and humorous, albeit very insightful and highly readable (Armstrong 1978). On the basis of a detailed review of the extant empirical research, Armstrong identified, described, and provided an evaluation of the various subjective and objective methods used in forecasting. Wit...

In order to improve the accuracy of grain production forecasting, this study proposed a new combination forecasting model, the model combined stepwise regression method with RBF neural network by assigning proper weights using inverse variance method. By comparing different criteria, the result indicates that the combination forecasting model is superior to other models. The performance of the ...

Introduction Forecasting demand is both a science and an art. Econometric methods of forecasting, in the context of energy demand forecasting, can be described as ‘the science and art of specification, estimation, testing and evaluation of models of economic processes’ that drive the demand for fuels. The need and relevance of forecasting demand for an electric utility has become a much-discuss...

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear autoregression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model’s performance with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear comb...