We consider the sources of forecast errors and their consequences in an evolving economy subject to structural breaks,forecasting from mis-specified, data-based models. A model-free taxonomy of forecast errors highlights that deterministic shifts are a major cause of systematic forecast failure. Other sources seem to pose fewer problems. The taxonomy embeds several previous model-based taxonomi...

To exactly forecast the urban rail transit passenger flow, a multi-level model combining neural network and Kalman filter was proposed. Firstly, ELAN neural network model was introduced to implement a preliminary forecast of the passenger flow. Then the Kalman filter was used to correct the preliminary forecast results, so as to further improve the accuracy. Finally, in order to validate the pr...

When the service region of ports overlap, consignors’ selecting behaviors for shipping ports become homogeneous to commuters’ choosing behaviors on trips. The commuters’ travel behaviors can be described through a probabilistic model in transportation planning. In this study, we adopt the transportation probabilistic forecast model to forecast port throughput. First, we amend the model with a p...

This paper studies forecasts when the forecast loss function is asymmetric, using Australian unemployment rates as an example. We focus on simple univariate models including autoregressive models and self-exciting threshold autoregressive models and we employ the same asymmetric quadratic loss function that is used for forecast evaluation at the model selection, model estimation and forecast co...

The purpose of this study is presenting a model forecasting financial crisis in Tehran Stock Exchange listed companies. To do this, productive firms that had been accepted in Tehran Stock Exchange between 2002 and 2009, were selected as the study sample. First the independent variables were obtained based on financial ratios and then based on Article 141 of the Law of Commerce, the insolvent an...

To make optimal decisions, end-users of decision support systems require information accurately describing the uncertainty of the underlying weather forecasts. Air temperature, dew point temperature, and wind speed are critical surface weather variables in many economic sectors. The generation of sharp and calibrated probabilistic forecasts and their effective presentation to decision makers ar...