In this video I’ll discuss about the theory of basic time series analysis. So we will learn what are the different types of econometrics models In time series forecasting So, we will basically learn what AR, MA, ARMA, ARMA OK? sO,

Time Management Skills

## 100 Comments

Helpful. Thanks

At 32:04 you have mentioned that differencing is actually differentiating. Isn't it actually subtracting with the order of the backward shift operator? Say first order differencing is actually yt – yt-1.

Hi , For Strictly stationary you told mean , variance and covariance are time invariant, that is constant. For weakly stationary also you have mentioned mean, variance should be constant. Can you please help me on this ?

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I really like your video, it was very enlightening. I'm also looking for some explanation on correlogram and Augmented Dickey- Fuller Test in Eviews analysis, not so much of the programing part itself. I tried to look it up, but I couldn't find any view from you. Is there any?

it was good. i need some information about time series and sarima models in stata. can you help me?

this was the best video on you tube of time series!! I from Perú, thanks!!

man, you could have given the presentation a 100 times better than the current one.. you've made an interesting topic sound like a boring s#%t

Very Nice..Thankyou

for arma model

ok p denotes to past value..fair enough

but what does q stand for, so do we just took random alphabet to refer to random error term

Thank you so much Sir 🙂 very well explained

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Thank You! Big help! 🙂

thank you, sir, excellent teaching please share this at [email protected]

Where is the death_Age.csv file?

Thankyou very much Sir!This has been very useful.

Good presentation, please kindly share your PPT with me at [email protected]

good

for god's sake, one ad every 5 minutes lesson.

Regarding white noise series,You have mentioned that mean of the series will be 0 for white noise series and then later told that time series shouldn't be applied and take the average of the series..If mean is 0 how does it matter of calculating average

Top' s my professor's lectures by a mile and a 1/2!!!

At 22:10, for white noise you told that the average is the best forecast. But isn't average = 0, for such white noise time series?

Good video. Nice clear explanations. I am taking time series/forecasting class and have tests coming up soon and this was a good summary of concepts. We use SAS in this class, I wish we used R as it is much easier

Thank u¡

good explanation of time series. But I would like to know which software is used.

Good tutorial , but is totally ruined by the really hard to understand handwriting.

Good introduction, thanks for sharing !

can u please share this ppt at

[email protected]

too much ad its boring now

Lot of distraction by adding zomato ads ….

Thank you for your work.

There are quite some things that can be improved.

The random drawings on the PPT is a little bit too much and distractive.

Another place for improvement is the examples given for each slide. There are times the speaker was thinking about examples. It is better to prepare a few examples for slides as a cheat-sheet like note before the speech. Recommend example style from Khan Academic.

The third place to improve is accent. Though this is a little bit too hard to improve in short time, please at least consider improving it in the future.

Thank you again.

Very good…

Great !

Holy cow! Ads every 3 mins! Jeez!

One small question. For white noise if the mean should be zero. How come the best forecast is Average? Couldn't understand the usage of mean and average here?

Can I get these slides?I am studying financial econometrics and this video is very useful.

Could please do a video on NSE stock exchange data usage moving average

watch the video at a speed of 1.25

The best explanation of time series. It's totally worth the 53 mins. However I have a doubt, when he said about the error in the MA method. Equation what you meant was: xty = slope*xt + intercept. and the error = xty – xt. Kindly correct me if I am wrong.

That was amazing and useful. Thank you. I wish I knew what resources are used in this video.

Time series analysis is one of most complicated topics for me and your video has simplified it so much for me. Your handwritten notes also helped a lot to understand the concept. Thank a lot for explaining in such a brilliant way!!!

Spikes decay toward zero; spikes cut off to zero; I wish that the meaning of this two expressions was made explicit.

BEST VIDEO EVER THANK YOU

Very easy to grasp and very well explained. Thanks a lot for the video!!

Good grief, as Charlie Brown would say. Wayyyy too much "ahh, ah,, ahhh" for my pitifully low tolerance. In other news, I see that his gf or sister has left a superlative comment below. The best on YouTube. Wow.

Are you an odia guy?

For a particular dataset, i found (p=0, i=1, q=2) and (p=0, i=1, q=3). In both cases i shifted the predicted values by -1 timeperiod.(what i predicted for feb, i conisdered it for jan). Doing so i got testing accuracy of 99% (MAPE-mean abs percetage err).

What went wrong with this scenario because 99% seems way to good to be correct for any scenario.

There are plenty of videos on youtube. If you don't like how he teaches, go find another one. That is the beauty of internet. For me, it served the purpose and now I understand time series. Thank you, very much.

awesome explanation…!!

I am new to time series, your video was helpful, it had all the concepts in one place and it was an hour well spent. However I don't think differencing and differentiating are the same thing. Differencing is the process of taking a difference of series from itself as various lags. I am not sure if its about taking a derivative (like you hinted) …

I have shared the video with my class mates. Thanks for putting this up. Where can I get to buy the package? Do you for a student price?

This truly is the best video on the subject. I have regression and time series exam this Saturday! This was a huge help!

Thank you so much sir. Your explanation has taught me enough about the time series model. Thank so much from Bhutan.

does anyone know anything about varima model?

this is really a good explanation of time series analysis for beginners .i learn a lot from this video..thank u very much sir

21:15, why would variance be constant?

Great video on timeseries modelling.

Lol WTF with the adds, here is your dislike!!

It took me too many minutes (over an hour) to watch this, just because of all the advertisements. Statistics is already not so fun for me, but you're making it worse in this way! This video is not workeable at all anymore. It's a shame, because you explain well in between the advertisements.

good presentation thank u

Differentiated once is said to be integrated by order 1? That is really confusing

By definition I time series has regular time intervals but how do I work on time series with irregular time intervals like sensor data coming at irregular time? Can I still use ARIMA, ARMA, MA models ?

your lecture is good and clear

One of the best video for learning econometrics basics… Thankyou soomuch.

best explanation for time series .. Thank you very much sir

what is difference between ARMA and ARIMA?

Please please please declare your values. Is beta a constant? integer? real? is Phi a constant? It would be helpful to use proper mathematical notation to make it clear!

A very easy and simple way of time series concepts explanation.. Great job

Sir, Your way of teaching awesome

Well-done! Very professional, informative, with a lot of contents, and answers almost every possible questions. In the future videos, please also explain specific letters. For example, if you say that "p". or t, or u, or whatever, please explain that those specific letters refer to. Thanks a lot. I took a lot of useful notes.

Thank you sir…!!

Nicely done! Throughout this session, you have articulated clearly why we need to perform certain steps. That was quite helpful for me.

https://youtu.be/Vih2Bw-aEbk

Time series analysis

If anyone is interested, I wrote some Javascript that does this: https://github.com/8483/time-series

the presentation is not sexy but the explanation are very good. Thanks

Amazing ! Thanks !

Good attempt

you save my time …thanks alot

Great video, very concise!

thank you for making this video.. i hope i will ace my exam tomorrow

You are my hero.

You save my ass for my Bachelor Thesis. Thank you very much !

This is the best video lecture for time series analysis on youtubr

Ek number. Best & crystal clear explanations. Keep up the good work.

Super theoretical not very intuitive

Thank you so much sir!! So far the best video on time series

Very well explained, must say.

Hello,

I am new to Forecasting specially ARIMA/ETS techniques. I have watched most of your videos on ARIMA/Time Series and like the way you explain in a simple and slow manner. I am still not able to fully grasp concepts and have lots of lots questions around ARIMA. I started doing some simple programming in R for ARIMA models. Attach small R script predicts US GDP for 2017 and 2018. I did try through both AUTO ARIMA and non Auto. Through non Auto ARIMA my error difference between Actual and forecasted are lower than AutoARIMA. Does this mean Auto Arima should not be trusted. Below is what I explain in R script. I will really appreciate your response. Thanks

#Actual 2017 and 2018 GDP Numbers

Year Actual

2017 59,928

2018 62,641

#Forecast with AutoArima

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95

2017 58824.37 58170.17 59478.56 57823.86 59824.87

2018 59790.69 58559.33 61022.05 57907.49 61673.89

#Forecast with Arima order 1,1,1

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95

2017 58987.01 58273.22 59700.79 57895.37 60078.64

2018 59972.21 58536.88 61407.54 57777.07 62167.36

Below is my R script

installed.packages("WDI")

library("WDI")

library(forecast)

gdp<-WDI(country = c("US"), indicator = c("NY.GDP.PCAP.CD"),start=1960,end=2016)

names(gdp)<-c("iso2c","country","GDPPerCap","year")

head(gdp)

#Order gdp in ascending order

gdp<-gdp[order(gdp$year),]

head(gdp)

plot(GDPPerCap~year,data=gdp)

#This ts function will transform data.frame into sequence of data record

us<-ts(gdp$GDPPerCap,start = min(gdp$year),end=max(gdp$year))

us

#Run Dickey fueller test

adf.test(us,alternative = "stationary")

#Run Auto correlation and Partial correlation function

acf(us)

pacf(us)

#Run Auto Arima

usOPT<-auto.arima(us)

usOPT

#Run Manual Arima

usOPT <-arima(us,order = c(1,1,1))

usOPT

#Question: Why manual setting order of 1,1,1 predict forecast for 2017&2018 with less error difference than auto arima which producess 2,2,2?

#See below results

#Coefficent

coef(usOPT)

predict(usOPT,n.ahead = 2,se.fit = T)

#Forecast

GDPForecast<-forecast(object = usOPT,h=2)

GDPForecast

#Actual 2017 and 2018 GDP Numbers

Year Actual

2017 59,928

2018 62,641

#Forecast with AutoArima

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95

2017 58824.37 58170.17 59478.56 57823.86 59824.87

2018 59790.69 58559.33 61022.05 57907.49 61673.89

#Forecast with Arima order 1,1,1

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95

2017 58987.01 58273.22 59700.79 57895.37 60078.64

2018 59972.21 58536.88 61407.54 57777.07 62167.36

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Lot of story

22:00 mins – If white noise is purely random, then how come it has a 0 mean and a constant variance? A specific mean and a constant variance makes a term determinable or predictable, right? Kindly help me on this. Thank you 🙂

Thanks for making this video tutorial. Many of my doubts are now clear.

good explanation

what an incredible tutorial! Please keep posting many more. Thanks and God bless you.

What if both ACF and PACF have spikes cut off to zero? Is that possible? Because I am running time series on R and my graphs are showing cut off to zero for both ACF & PACF.

How can I download the dataset used in case study i.e. death_Age.csv

It is very nice presentation style. It help students to understand what main points should be understood.

Amazingly explained sir

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