The Damped Trend (DT) forecasting method has been recognized for its superior accuracy. Li et al., (2014) show when DT forecasts are used within the order-up-to (OUT) policy, the bullwhip effect is avoided by using unconventional DT parameter settings. We extend this study in three directions. First, by investigating the relationship between the stability and invertibility, we show that stable ...

In order to make accurate forecast of mobile Internet diffusion trend. This paper proposes a method which is based on Bass innovation diffusion model. To solve the problem that the parameters of the model are difficult to estimate, a modified particle swarm optimization algorithm (PSO) whose inertia weight changes dynamically is introduced to search the most precise parameters. The application ...

Empirical research documents that temporary trends in stock price movements exist. Moreover, riding a trend can be a profitable investment strategy. Thus, the ability to recognize trends in stock markets influences the quality of investment decisions. In this paper, we provide a thorough test of the trend recognition and forecasting ability of financial professionals who work in the trading roo...

The fashion industry faces serious challenges in terms of accurate demand forecasting. While production decisions have to be made at an early stage, precise demand information only become available several months later. One main characteristic of the fashion industry is long time-to-market compared to short selling periods. Consequently, it is hardly possible to replenish successful products. T...

Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Cov...

Recently, novel learning algorithms such as Support Vector Regression (SVR) and Neural Networks (NN) have received increasing attention in forecasting and time series prediction, offering attractive theoretical properties and successful applications in several real world problem domains. Commonly, time series are composed of the combination of regular and irregular patterns such as trends and c...

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, ...

I n the present study Iran's rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks ...

The trend of time series characterize the intermediate upward and downward patterns of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers and load schedule in smart grid. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel hybrid neural ne...

Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute per...