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Traditional versus novel forecasting techniques: How much do we gain?
Viviana Fernandez
Business School
Research output
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Contribution to journal
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Article
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peer-review
11
Scopus citations
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Dive into the research topics of 'Traditional versus novel forecasting techniques: How much do we gain?'. Together they form a unique fingerprint.
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Keyphrases
Support Vector Machine
100%
Wavelet
100%
Forecasting Techniques
100%
Unobserved Components
66%
Artificial Neural Network
33%
Prediction Accuracy
33%
Forecasting Combination
33%
Seasonal Autoregressive Integrated Moving Average (SARIMA)
33%
U.S. Manufacturing
33%
Novel Technique
33%
Encompassing Test
33%
Test Combination
33%
Autoregressive Integrated Moving Average (ARIMA)
33%
Accuracy Test
33%
Popular
33%
INIS
forecasting
100%
gain
100%
vectors
75%
comparative evaluations
25%
accuracy
25%
neural networks
25%
values
25%
information
25%
economics
25%
manufacturing
25%
shipment
25%
Arts and Humanities
Tradition
100%
Technique
100%
Unobserved components
66%
User-friendly
33%
Popular
33%
Artificial
33%
USA
33%
Economics, Econometrics and Finance
ARMA Model
100%
Finance
50%
Time Series
50%