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Forecasting US GDP Growth with ARIMA & ETS Models in R
This report forecasts US GDP growth rate using 64 years
of quarterly Federal Reserve data (1960-2023).
Two models are built and compared:
- ARIMA — captures autocorrelation structure
- ETS — adapts to structural shifts post-COVID
Key findings:
- GDP growth is mean-reverting (~2.5% long run average)
- ARIMA performs better in stable economic periods
- ETS adapts faster during volatile periods
- Both models significantly outperform naive forecasts
Tools used: R, forecast, tseries, Quarto
session_229
fixed \alpha in all rounds, deterministic energy price, table version (no risk, no loss), saving treatment
Implementasi Principal Component Analysis (PCA) dan Factor Analysis (FA) pada Dry Bean Dataset
Untuk memenuhi tugas mata kuliah Analisis Multivariat
Nama: 1. Alifiyanti Putri Nur Azizah (24031554032) 2. Muhammad Zikri Widiandra (24031554088)
Kelas: 2024B
Dosen Pengampu: Dinda Galuh Guminta, M.Stat.
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