Chapter 1 Time series

This book contains the codes for Time Series 1 and 2. By the end of these two courses, you should be able to:
* Create plots and clean time series data
* Understand format of time series data
* Identify the different characteristics of a time series (trend, cycle, seasonality, etc)
* Perform a time series decomposition and understand the different decomposition techniques
* Perform several exponential smoothing models
* Understand stationarity and how to test for it
* Understand and be able to use ACF and PACF plots to develop potential ARMA models
* Be able to fit basic ARIMA models
* Identify if white noise exists
* Identify seasonality within time series data sets
* Build seasonal ARIMA models
* Incorporate external variables into ARIMA models (dynamic regression)
* Fourier transformations in time series models
* Build Facebook Prophet models
* Build Neural Network Autoregressive models
* Ensemble different time series models
* Construct hierarchical time series approaches

All of the data sets needed for this class are on the github repository. Enjoy your TIME in Time Series!!