Forecasting with Big Data: A Review
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Summary
The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data.
- Type
- review
- Published
- 2015-03-01
- Cited by
- 135
- References
- 108
- Access
- Open access
- OpenAlex
- https://openalex.org/W1976239864
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:58311097
Keywords
Big data, Data science, Computer science, Process (computing), Artificial neural network
References
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- Business modeling and data mining
- Large-scale inference
- Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies
- Calculating economic indexes per household and censal section from official Spanish databases
- Forecasting Large Datasets with Conditionally Heteroskedastic Dynamic Common Factors
- Forecasting Government Bond Yields with Large Bayesian VARs
- The Methodology and Practice of Econometrics
- Big Data: Principles and best practices of scalable realtime data systems
- Data Mining and Official Statistics: The Past, the Present and the Future
- Bayesian regression mixtures of experts for geo-referenced data
- Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis
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- Combining official and Google Trends data to forecast the Italian youth unemployment rate
- Saving costs with a big data strategy framework
- What Can Massive Mobile Phone Data Tell Us About International Trade?: The Case of Spain
- Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk
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