Time Series Forecasting in R #13

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Week-13
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.

Dr. Bharatendra Rai

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As always, I learn a lot from your videos. Clear explanation and so easy to digest. Thank you, professor!

LyLy-vifw

Always brilliant professor. Every minute I learn something new with you.

dilshadsaeed

How it is an awesome tutorial! Great thanks!❤

DB-kvwu

Thank you very much...I have learnt a lot from your would like to learn all binary models probit, survival analysis with r

hoseamutwiri

Thank you for this informative session, could you please do time series forecasting using LSTM

maryamhelae

Thank you Prof. For your good lectures. Could you please talk about forecasting in the presence of structural breaks in the data?

daudaliaser

Hello sir ! I would like to thank you immensely as your videos have been really helpful in understanding statistical modelling in R. If possible, could you please cover advanced time series models like ARDL, VECM and their forecasting in R.

HEMANATHRAMMIPMBatch

Thank you Sir for explaining concepts in such simple and useful manner. Thanks a lot for your videos. Can I request you to create some videos on bayesian statistics and monte carlo simulation in R ?

pravirsinha

hi sir, can we use ARIMA if your data is just for 2 years? what is your recommendation on this?

heartquilaneta

Do we need stationary data variables for multiple regression models?

mihikapathak

Sir, can you please do the same with Air pollution data, that will be very helpful for my academic project

nambiharishce

Sir can you please make a classs forecasting by using garch models

srinivasulusrinu

sir I cannot able to view file in datahub. can you direct or upload file sir.

balajisubramanian

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