Forecasting as a scientific discipline has progressed a lot in the last forty years, with Nobel prizes being awarded for seminar work in the field, most notably to Engle, Granger and Kahneman. Despite these advances, even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners: “what is the best method for my data?”. In...

Forecasting is arguably the most critical component of airline management. Essentially, airlines forecast demand to plan the supply of services to respond to that demand. Forecasts of short-term demand facilitate tactical decisions such as pricing and seat inventory control-the allocation of seats among the various booking classes. In this study, an evaluation was conducted of the relative perf...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

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Received Jul 22, 2012 Revised Oct 23, 2012 Accepted Nov 14, 2012 Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forec...

State-space or latent-variable models for stock prices specify a process for expected returns and expected and unexpected dividend growth, and then derive dividend yields and returns from a present value relations. They are a useful structure for understanding and interpreting forecasting relations. In this note, I connect state-space representations with their observable counterparts, and VAR/...

Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic fore...

gasoline is the most important energy product in the passenger transportation sector in iran. gasoline demand survey has a high priority in iran because of its ever-increasing consumption. the most important challenges for this purpose are uncertainties resulting from structural failure of economy, changing of policies and lack of accurate data. this research aims to specify the variables expla...

  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...

Predictions of call center arrivals are a key input to staff scheduling models. It is, therefore, surprising that simplistic forecasting methods dominate practice, and that the research literature on forecasting arrivals is so small. In this paper, we evaluate univariate time series methods for forecasting intraday arrivals for lead times from one half-hour ahead to two weeks ahead. We analyze ...