Week-13
R File: https://github.com/bkrai/Statistical-...
Time series data;
Example-1: Forecasting international airline passengers;
Example-2: Forecasting daily Apple stock price;
TIMESTAMPS
00:00 Introduction
01:40 Steps for forecasting with time series data
05:40 Working in R - Forecasting international airline passengers
10:50 Log transformation
12:00 Scatter plot of lags
13:40 Autocorrelation function (ACF)
15:27 Partial autocorrelation function (PACF)
18:25 Differencing to make time series data stationary
22:00 Decomposition of time series data
23:25 Autoregressive integrated moving average (ARIMA) model
25:40 Model output interpretation, seasonality and non-seasonality parts
33:55 Box-Ljung test
35:05 Residual plot
35:35 Forecasting international airline passengers
37:00 Forecast airline passengers in original units
39:15 Obtaining and understanding daily Apple stock price data
43:45 ARIMA model using daily Apple stock price data
46:30 ACF, PACF, Box-Ljung test, residual plot
50:30 Forecasting daily Apple stock price
52:54 Forecast daily Apple stock price in original units
54:22 Question/answers

R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.