ARIMA and Time Series Forecasting Table of Contents


Table of Contents
 


Time Series Basics

  • What is a Time Series?
  • Modeling vs. Predicting
  • Power, Log, and Box-Cox Transformations

Financial Time Series

  • Financial Time Series Basics
  • Random Walks and the Random Walk Hypothesis
  • The Naive Forecast and the Importance of Baselines

ARIMA

  • ARIMA Introduction
  • Autoregressive Models - AR(p)
  • Moving Average Models - MA(q)
  • ARIMA
  • ARIMA in Code
  • Stationarity
  • Stationarity in Code
  • ACF (Autocorrelation Function)
  • PACF (Partial Autocorrelation Function)
  • ACF and PACF in Code
  • Auto ARIMA and SARIMAX
  • Model Selection, AIC and BIC
  • Auto ARIMA in Code
  • ACF and PACF for Stock Returns
  • Auto ARIMA in Code (Sales Data)
  • How to Forecast with ARIMA
  • Forecasting Out-Of-Sample

Setting Up Your Environment

  • Pre-Installation Check
  • Anaconda Environment Setup
  • How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow

Python Coding for Beginners

  • How to Code Yourself
  • Proof that using Jupyter Notebook is the same as not using it
  • How to use Github & Extra Coding Tips

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