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...
Time series forecasting is an area of research within the discipline of machine learning. The ARIMA ...
The increasing availability of large amounts of historical data and the need of performing accurate ...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable...
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 ...
Managerial intuition is a well-recognized cognitive ability but still poorly understood for the purp...
The development of machine learning research has provided statistical innovations and further develo...
In this article, we shed light on the differences between two judgmental forecasting approaches for ...
Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have play...
Whilst considerable research shows that people tend to underestimate their task completion times, th...
In the field of cognitive science, the primary means of judging a model’s viability is made on the b...
One of the challenging questions in time series forecasting is how to find the best algorithm. In re...
Mathematically speaking, time series are sets of observations that are generated sequentially over t...
A number of studies in the last couple of decades has attempted to find, in terms of postsample accu...
Time series forecasting is an area of research within the discipline of machine learning. The ARIMA ...
The increasing availability of large amounts of historical data and the need of performing accurate ...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable...
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 ...
Managerial intuition is a well-recognized cognitive ability but still poorly understood for the purp...
The development of machine learning research has provided statistical innovations and further develo...
In this article, we shed light on the differences between two judgmental forecasting approaches for ...
Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have play...
Whilst considerable research shows that people tend to underestimate their task completion times, th...
In the field of cognitive science, the primary means of judging a model’s viability is made on the b...
One of the challenging questions in time series forecasting is how to find the best algorithm. In re...
Mathematically speaking, time series are sets of observations that are generated sequentially over t...
A number of studies in the last couple of decades has attempted to find, in terms of postsample accu...
Time series forecasting is an area of research within the discipline of machine learning. The ARIMA ...
The increasing availability of large amounts of historical data and the need of performing accurate ...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...