Intro Reconciliation Dataset Feature Engineering Models Evaluation ARIMA ARIMA vs ETS Performance for each level Cluster level National level Conclusion Error Intro The aim of this series of blog is do predict monthly admissions to Singapore public acute adult hospitals. The dataset starts from Jan 2016 and ends in Feb 2021. EDA for the dataset was explored in past posts ( part 1 ; part 2 ).
Introduction 1. Lags 1.1 Auto-correlation (ACF) 1.2 Partial Autocorrelation (PACF) 2. Correlation of features Hospital level Cluster level National level All levels Correlation findings 2.1 PCA of features 2.1 Variation captured 2.2 Time series features contributing to PC1 2.3 Distribution of hosptials in PC plane Conclusion Errors Introduction The aim of this series of blog is to predict monthly admissions to Singapore public acute adult hospitals.
Introduction Visualization 1. Trend 2. Seasonality Trend and seasonality 3. Anomaly Conclusion Introduction The aim of this series of blogs is to do time series forecasting with libraries that conform to tidyverse principles and there are two of these time series meta-packages modeltime which is created to be the time series equivalent of tidymodels fpp3 which is created to do tidy time series and has been nicknamed the tidyverts.