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The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).

Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.

The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.

The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.

The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.

This is the first study to apply the NNAR model to construction output forecasting research.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

Earnings forecasts provide useful numerical information concerning the expectations of a firm's future prospects and indicate management's ability to anticipate a firms changing internal structure and external environment. The accuracy of these earnings forecasts that has been given so much attention is due to the S.E.C.'s position on financial forecasts and the issuance of the Statement of Position by the AICPA. These statements are important since they, in part, have motivated researchers to the importance of forecasting financial information. Consequently, if the disclosure of earnings forecasts in financial reports is permissable, the improvement of financial forecasts should be one of the primary concerns of the AICPA, the SEC, and numerous other interested groups.

Price stabilization in international commodity markets is a main element of the North‐South dialogue. Within the Integrated Programme on Commodities (IPC) of UNCTAD, it is intended to create buffer stocks for 10 core commodities: sugar, natural rubber, cocoa, coffee, tea, cotton, jute, hard fibres, copper, and tin. Several theoretical studies justify these plans by stressing the positive effects of a functioning buffer stock scheme on different economic goals. It is argued that price stabilization will, “potentially at least, improve aggregate welfare” (Turnovsky, 1978, p. 143) and that risk benefits in the case of risk‐averse producers “will be far more important” (Bigman, 1982, p. 1984; on the concept, see Newbery/Stiglitz, 1981, pp. 267 et seq.) than the transfer benefits, if income uncertainty is reduced by the stabilization policy. Other positive effects of buffer stocks are stressed with respect to food security (Bignan, 1982, pp. 129 et seq.) and, except for the case of supply‐induced fluctuations and a price elastic import demand, with respect to the stability of export earnings (Nguyen, 1980, pp. 343 et seq.). The export earnings stabilizing effect as well as a mostly earnings‐raising effect is confirmed for several core commodities by simulation analyses (Behrman/Ramangkura, 1978, p. 166) and by dynamic optimization (Lee/Blandford, 1980, p. 385). Moreover, stable export earnings of less developed countries (LDCs) are expected to induce higher growth rates of GNP than unstable ones (Lim, 1976, pp. 311 et seq.).

This study investigates the time‐series properties of operating cash flows per share and earnings per share for all manufacturing firms on the Compustat Quarterly Industrial tape for which sufficient data are available. Both individually‐identified and “premier” models are compared on the basis of their relative fit and forecasting accuracy. The empirical results suggest that for both accounting variables the individually‐identified models outperform the premier models, although this advantage is larger for earnings, and for forecast horizons beyond one quarter ahead. A major conclusion of the study is that the time‐series properties of cash flows are quite different than those of earnings. In particular, the cash flow series are considerably less predictable, as shown by their relatively high incidence of white‐noise series and relatively large forecast errors.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.

The emergence of advanced manufacturing technologies such as Flexible Manufacturing Systems (FMS) is forcing organisations to re‐examine their manufacturing strategies. CNC machines are an integral part of FMS. The literature dealing with the downtime behaviour of these machines is sparse. The purpose of this article is to analyse the behaviour and forecast downtimes of these machines using Box‐Jenkins time series analysis. It is concluded that the models fitted to the data are appropriate, and the results of this study can be used in production planning.

The purpose of this paper is to examine the performance of the vector error‐correction (VEC) econometric modelling technique in predicting short‐ to medium‐term construction manpower demand.

The VEC modelling technique is evaluated with two conventional forecasting methods: the Box‐Jenkins approach and the multiple regression analysis, based on the forecasting accuracy on construction manpower demand.

While the forecasting reliability of the VEC modelling technique is slightly inferior to the multiple log‐linear regression analysis in terms of forecasting accuracy, the error correction econometric modelling technique outperformed the Box‐Jenkins approach. The VEC and the multiple linear regression analysis in forecasting can better capture the causal relationship between the construction manpower demand and the associated factors.

Accurate predictions of the level of manpower demand are important for the formulation of successful policy to minimise possible future skill mismatch.

The accuracy of econometric modelling technique has not been evaluated empirically in construction manpower forecasting. This paper unveils the predictability of the prevailing manpower demand forecasting modelling techniques. Additionally, economic indicators that are significantly related to construction manpower demand are identified to facilitate human resource planning, and policy simulation and formulation in construction.

In contrast to recent forecasting developments, “Old School” forecasting techniques, such as exponential smoothing and the Box–Jenkins methodology, do not attempt to explicitly model or estimate breaks in a time series. Adherents of the “New School” methodology argue that once breaks are well estimated, it is possible to control for regime shifts when forecasting. We compare the forecasts of monthly unemployment rates in 10 OECD countries using various Old School and New School methods. Although each method seems to have drawbacks and no one method dominates the others, the Old School methods often outperform the New School methods for forecasting the unemployment rates.