Abstract This paper enters the ongoing volatility forecasting debate by examining ability of a wide range Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric for ten Asian markets using daily data time period from 12 September 1994 to 05 March 2018. empirical results indicate that ML algorithms, acro...

F ollowing World War II, the quantity and quality of macroeconomic data expanded dramatically. The most important factor was the regular publication of the National Income and Product Accounts, which contained hundreds of consistently defined and measured statistics that summarized overall economic activity. As the data supply expanded, entrepreneurs realized that a market existed for applying ...

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and,...

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particula...

Environment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire – MEC), which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC – Surface and Hydrology), a configuration of MEC cu...

Timber price forecasts are important components of timberland investment analysis. Two techniques are widely used in forecasting. Econometric models use one or more independent variables to predict a dependent variable. Time series analysis or autoregression techniques use the prior observations of a variable, and only those prior observations, to make predictions of that variable. Either techn...

Recent research carried out in CRES has produced a data-based modelling approach to the adaptive forecasting of river flow or levels using rainfall or upstream flow inputs as leading indicators. This approach, which is based on the estimation of a general transfer function model from historical data, has significant advantages over existing flood forecasting methods: for example, on-line adjust...

[1] Nowadays none of the operational daily forecasts in Europe includes the influence of Saharan dust on a nonclimatic basis. In order to account for this, the BSC-CNS currently operates daily photochemical forecasts in the Iberian Peninsula with MM5-EMEP-CMAQ modelling system and Saharan dust forecasts over Southern Europe with Eta/ DREAM. The necessity of coupling both modelling systems is ad...

Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives most accurate results? Econometric can deal with different types of time series and good results, but specific cases, they may fail to provide predictions. Recently, new techniques borrowed from soft computing area were adopted for economic forecasting. S...