the main purpose of the present research is to determine the relationship between the management earnings forecast errors and conservatism level and then surveying about the effects of forecast difficulty, and external financing on this relationship. regarding this, the financial information related to 147 stock firms, available during the period of study (2003-2015) were collected and analyzed...

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) ...

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...

Knowledge on the contribution of observations to forecast accuracy is crucial for the refinement of observing and data assimilation systems. Several recent publications highlighted the benefits of efficiently approximating this observation impact using adjoint methods or ensembles. This study proposes a modification of an existing method for computing observation impact in an ensemble-based dat...

In this paper, the concept of long memory systems for forecasting is developed. The Pattern Modelling and Recognition System (PMRS) and Fuzzy Single Nearest Neighbour (SNN) methods are introduced as local approximation tools for forecasting. Such systems are used for matching current state of the time-series with past states to make a forecast. In the past, the PMRS system has been successfully...

This paper presents a distributed model that is in operational use for forecasting flash floods in northern Austria. The main challenge in developing the model was parameter identification which was addressed by a modelling strategy that involved a model structure defined at the model element scale and multi-source model identification. The model represents runoff generation on a grid basis and...

In the paper a Nonlinear Set Membership (NSM) prediction method is applied to forecast tropospheric pollution. The NSM method does not require the choice of the functional form of the model used for prediction but only assumes a regularity condition on the regression function defining the model. In this way, the complexity/accuracy problems deriving from the proper choice of a suitable parametr...

Traditionally, the quality of a forecasting model is judged by how it compares, in terms of accuracy, to alternative models. However, by providing a relative measure, no indication is given as to how much scope there might be for improvements beyond the benchmark model. When judgemental methods are used alongside simple forecasting models, the scope for such improvements is considerable and dif...

Time series models based upon the reported accounting data of Abbott Laboratories Inc. are used to forecast its one-year-ahead raw returns over hold-out periods of 1, 4 and 10 years. The sample data is annual, from 1955 to 2003. A general-to-specific, testing down, modelling method is used. The models suggest market values are corrected for imbalances between expected and actual market values i...

The potential of high-density observations is studied in a practical context of the 4DVAR assimilation. A series of observing system simulation experiments (OSSEs) are carried out. Observations with both uncorrelated and correlated observation errors are simulated in sensitive areas. The results show that: for the observations with uncorrelated error, increasing the observation density generall...