Diego Antonio Bittencourt Marconatto
Biographic Data
| ID | 1232239 |
|---|---|
| NAME | Diego Antonio Bittencourt Marconatto |
| GIVEN NAMES | Diego Antonio Bittencourt |
| FAMILY NAME | Marconatto |
| SIGNATURE | MARCONATTO D A B |
| AFFILIATIONS | Fundação Dom Cabral |
| ORCID | 0000-0002-9458-9199 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Implementation of the Arima model for prediction of economic variables
In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly…
Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction
Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating …
No prominent works on this page.
Implementation of the Arima model for prediction of economic variables
In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly…
Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction
Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating …
Computer Science (2 works) · Actuarial science (1 work) · Algorithm (1 work) · Artificial Intelligence (1 work) · Autoregressive integrated moving average (1 work) · Box–Jenkins (1 work) · Business (1 work) · Climate Change and Health Impacts (1 work) · Econometrics (1 work) · Economic growth (1 work)