Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Previsão Conforme para Previsão de Séries Temporais× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2021 | 2015 |
| Autor original≠ | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Box & Jenkins (Box-Jenkins methodology) |
| Tipo≠ | Distribution-free prediction interval wrapper | Univariate time-series model |
| Fonte seminal≠ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Outros nomes≠ | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023). | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateConjunto de dados ↗ |
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