Add to ORKGCognitive models have been paramount for modeling phenomena for which empirical data are unavailable, scarce, or only partially relevant. These approaches are based on methods dedicated to preparing experts and then to elicit their opinions about the variables that describe the phenomena under study. In time series forecasting exercises, elicitation processes seek to obtain accurate estimates, overcoming human heuristic biases, while being less time consuming. This paper aims to compare the performance of cognitive and mathematical time series predictors, regarding accuracy. The results are based on the comparison of predictors of the cognitive and mathematical models for several time series from the M3-Competition. From the results, one ca...
Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have play...
This thesis is about forecasting situations which involve econometric models and expert intuition. T...
Creating accurate forecasts to inform planning processes and organisational decision making is a per...
Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable...
In this article, we shed light on the differences between two judgmental forecasting approaches for ...
Managerial intuition is a well-recognized cognitive ability but still poorly understood for the pur...
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to stati...
<div><p>Machine Learning (ML) methods have been proposed in the academic literature as alternatives ...
Time series processes are important in several sectors like marketing, transport, energy, telecommun...
Made available in DSpace on 2016-08-10T10:40:27Z (GMT). No. of bitstreams: 1 ANA PAULA DE SOUSA.pdf:...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
This paper presents an empirical exercise in economic lorecast using traditional time series methods...
Mathematically speaking, time series are sets of observations that are generated sequentially over t...
The performance of neural networks and statistical models in time series prediction is conditioned b...
The development of machine learning research has provided statistical innovations and further develo...
Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have play...
This thesis is about forecasting situations which involve econometric models and expert intuition. T...
Creating accurate forecasts to inform planning processes and organisational decision making is a per...
Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable...
In this article, we shed light on the differences between two judgmental forecasting approaches for ...
Managerial intuition is a well-recognized cognitive ability but still poorly understood for the pur...
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to stati...
<div><p>Machine Learning (ML) methods have been proposed in the academic literature as alternatives ...
Time series processes are important in several sectors like marketing, transport, energy, telecommun...
Made available in DSpace on 2016-08-10T10:40:27Z (GMT). No. of bitstreams: 1 ANA PAULA DE SOUSA.pdf:...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
This paper presents an empirical exercise in economic lorecast using traditional time series methods...
Mathematically speaking, time series are sets of observations that are generated sequentially over t...
The performance of neural networks and statistical models in time series prediction is conditioned b...
The development of machine learning research has provided statistical innovations and further develo...
Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have play...
This thesis is about forecasting situations which involve econometric models and expert intuition. T...
Creating accurate forecasts to inform planning processes and organisational decision making is a per...