tures. Include ACF, PACF, and manual differencing plots in your submission. Do not use any functions that estimate model degree, estimate model degree manually. Fit each model to your data and print model diagnostics. Transform your time series as need

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Use a time series data set to do visualizations and model diagnostics to build the best model possible of the following model structures. Include ACF, PACF, and manual differencing plots in your submission. Do not use any functions that estimate model degree, estimate model degree manually. Fit each model to your data and print model diagnostics. Transform your time series as needed before modeling.

  • A pure autoregressive model, AR(p)
  • A pure moving average model, MA(q)
  • An autoregressive moving average model, ARMA(p,q)
  • A ARIMA(p,d,q) model

Use any functions to estimate model degree, such as forecast::auto.arima(). Fit the model. Make a paragraph on which model is best and why.

 
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