You are a born teacher. Most professors, teachers, lecturers teach to a limited audience, like brightest students. Your teaching targets 100% of the audience.

The theory is sharp and precise and the examples are simply to the point. This 3 part video is easy to understand and practise. Highly recommend this time series tutorial

Thank you so much! You are an amazing teacher! My teacher is dull and just clicks the buttons in class without really explaining anything. He goes so fast that I don't have time to form questions. This was a great video explaining time series forecasting, that I have to know for my exam next week. You rock!

It has been an amazing experience watching your video and learning..... because I believe you are an amazing teacher !.... because you know your subject inside out .... just like the back of your palm ! It is very kind of you to have made this exceptional 3 series video for people who know nothing about this. Thanks on behalf of every one and God bless !

Excellent series of videos, makes time series easier to understand. Thank you!!!

I don't usually comment on videos but this one deserves all the positive comments and likes . You are a very good teacher. 👏👏

This is brilliant.......Application component explained the simplest possible way!!!!!!!! I knew nothing about time series data but this video is illuminating........I read more than 20 papers to understand ARIMA model and no paper could better explain than this video........ Thank you from Nepal

This is a GREAT SERIES! Thanks so much!

Statistics is complicated, but speacial sound and visual effect with the perfect teaching technique, you made it look so easier. i loved this video, thank you

Excellent video! I look forward to viewing others.

God bless you sir 😂🙏🏽🙏🏽 I literally don't understand my lecturer who has a foreign accent, I'm so confident after watching your video 💯💯💯💯💯🙏🏽😭😭😭

cant wait to see the monthly forcast, thanks for your sharing.

WONDERFUL explanation! 👏🙌

the most amazing excel tutorial I have ever seen!

Great video and very clear explanation! By reading comments below, i finally figure out where the "It" migrates to. Maybe it's better to mention that in your video and there will be less confusion. However, that does not change the fact that it's a well structured and detailed video tutorial. Thank you!

Your videos are excellent to explain the method...thanks and congratulations!!

Thank you very much. Excellent work!

Thank you for excellent discussion and adding some theory.

Thank you so much this was so helpful!

Thank you, this is going to help me a lot!

thank you very much please upload more videos like these, they are very helpful!!!!

VERY CLEAR AND UNDERSTANDABLE!

You have no idea how much you helped me

Man you saved my life!!! or Lecturer was tying up our brains man Thank You !! You do Queuing?

Fantastic explanation. could not get any clearer than that!

Thank you so much!! It's really helpful

Excellent content and instructions

thank u for this good lesson

U r awesome! I finally understood

u r the best ever

10 years later and still saving people.

thank you very much. easy to understand.

Simply excellent

Thanks for these wonderful tutorial.I took same sales values, loaded in r and decompose to check trend,seasonality and irregular components and found the values are different from what you are showing here. Any idea how decompose function in r calculates these components?

superbly fantastic

Thank you this help me a lot

Thank you lesson_02 completed.

You are a great teacher . I'm comfortable with R because of your videos. Btw, have you created a time series using R video?

It is wonderful, thank you very much! can you suggest: what attitude of forecasting suitable for forecasting sales in 400 store in shops?

Thanks a lot

if you have monthly data, is it recommended to use a 12 month moving average? I noticed you said you selected MA(4) because the data was quarterly

thanks a lot!

awsome stuff bro :)

thank you for the video, and can you put up an exponential smoothing model? :)

just perfect

Thank you @Jalayer Academy, this video gives some insights. But I have one question. What if my data is a yearly time series data, which has no seasonal pattern like in the video? . Thank you so much, I hope you respond this one question with some answers.

Thanks for the vid! Will this work if I happened to have only 1 year of data?

Is it true that as long as i have quarterly data (sales/index etc), i can use this method for forecasting?

The four quarterly means should theoretically add to 4, because the average is set to 1. The total of the four may not be equal to 4 due to rounding. Like in this case the add to 4.007 In such cases the correction factor is then applied to each of the 4 means to force them to total 4. This is just a small addition, it must be noted that small deviations also affect the precision of decision making.

@presncubez

8 жыл бұрын

+Presley Ncube correction factor = 4/4.007 = 0.9983 then we multiply all these four seasonal indexes

Jalayer, thanks for the amazing videos! I was just wondering how to calculate or isolate the seasonal component in the calculations when the sample data in not in the format in your video. In order words, if the data in hence there's no quarterly markers as in the data set in your video. How does one account for the seasonality in the calculations using the classical multiplicative model ? Thanks

12:53 - 13:00 - like your effects

glad this helped

What if the unpredictable happens and there is a hail storm or something that hinders the sales of each quarter into year 5? How do you reflect that? I know you can predict the likelihood based off of empirical observation, but where does that come into play in adjusting the analysis?

1 year might be a little too few data points to get anything meaningful, you may still be able to get some results

Can we forecast the sales for the year 6,7,8 and so on based on this analysis?

watch video 1 in this series of 3 videos and/or pick up a statistics textbook with a chapter on Time Series Forecasting, hope this helps

I have set this up as a model for Months (Jan-Dec) rather than Quarters. I used MA(12)/CMA(12) and started the MA at t7 (July). Is the principle the same, so the steps can be followed the exactly?

My thought is along the same lines as Karthik's. What if the sales figures were annual instead of quarterly? It would be helpful if we could download example worksheets.

sehr gut beschreiben / very well described

Which playlist is this video part of?

If you're doing the multiplicative model for time series, does this mean that this doesn't relate to the the mixed model for time series??

is there a video where i can learn more about time lags and first differences etc?

Can you explain how you got to the seasonal average?

You get rid just from sezonality at the end. Irregularity remains, if I understand well.

your video helps me a lot. but how can i do a forecast for weekly data? by using 13 weekly data to forecast for the next 1 week demand. thanks :)

What model does this solution fit to? Because I am kinda confused here. I have been working with additive systematic component(level+trend+seasonal factor) and with models such as Moving Avg, Simple Exponential Smoothing, Holts Model and Winter's model. I feel like your solution is Winter's model which i dont have much info about. But you are using all in one thats why im confused. Could u help me figure it out?

Is this method recomended when time period is monthly and not by quarter?

How can work with you on a market share forecast? Thanks

This is an excellent video. You didn't scale your seasonal index. Why is that? Add up the values to see if they add up to 4 exactly. If they do not then adjust them. St X (4 / actual sum).

Hi. Jalayer. Can you please run this forecasting on epidemic data. I mean starting from case zero here will be no seasonality butbtrend and irregularity factors will be there

nice video.. ty :)

which criteria do you use to select a 4 point moving average over say 3 point moving average

@carlosescudero2302

5 жыл бұрын

seasonality...

Mine is showing the blank cells as zero? help

what is the difference between smoothing and deseasonality ,both values are almost the same

Dr.Jalayer! Help, please! Can I do time series forecasting with only yearly data? Does that mean I don't have to do the CMA step? Thank you so so much!

in a data such that revenues of years ( 2012 - 2013 - .... - 2019) we do not need to: 1- calculate MA(4) and CMA(4) right?? 2- Yt/MA(4) will be the same revenues right?

How does one isolate the irregular or random component?

Can anyone help me to understand, how averaging same time period of every year will get rid of "It" from "St,It"?

For column H - St - couldn't you simply use averageif and not create a new table to the side? =AVERAGEIF($C$4:$C$19,C4,$G$6:$G$17) 10:50

Can you make more videos on time series?

If I am doing for week over week data then how can I select my MA, CMA and build the seasonal component table ? Should I use 4 weeks ?

@exerblank

3 жыл бұрын

I'm wondering the same thing.

What would you modify if you had similar data but by month?

@alecryan8220

5 жыл бұрын

Change Quarter to month/period and create 12 rows per year

In the last step, u deseasonalised the data, Yt/St and irregularity component is still there, but u mentioned that we got rid of irregularity..

Frustratingly, I can't get my forecast trend line to mirror my existing data. I have a feeling because its th elinear regression formula - my data simply isn't linear! :(

thanks a lot,can up please share any references for these videos

Why year 2 quarter 2 is below 16%? how to get there?

Thank you from2022

my data shows like this intercept: 3.3829056 and t : 0.00888142 and p value of intercept :4.4276E-06 and that p value of t: is 0.857206584 what do it do? should i carry on or what else... cause my p value for slope is greater than .05 but that of intercept is not,,,,,, please answer me plzzz

How come there's not Cyclical component?

The professor should just say this and the three credit course will be a simple 1 credit course

hi, everyone. Can i forecast with Excel 2007, or 2010? I need a software to forecast demand of Call Center.

what is the name of this model????

What if you don't have a seasonal component (set pattern) ? How do take out the seasonality ?

@rdjalayer

9 жыл бұрын

Jason Mathew If there is no seasonality there is none to get rid of you can estimate the trend component.

@rtwol4063

8 жыл бұрын

+Jalayer Academy What if there are no seasonality but I have irregularities?

So my questions is, how come everywhere i look the forecast is always in the Qtr? Is this method not working for monthly sales?

@rdjalayer

4 жыл бұрын

Sure, monthly is very common too. Im guessing the reason why people don't use it to teach the concepts of forecasting is the lengthy nature of dealing with 12 month seasonal components versus just 4 quarterly ones. Otherwise, same principles.

Sebab dia seasonal secara sekata

n22 cell?

Do you have a video on using Monthly data year over year instead of Quarterly?

@rdjalayer

8 жыл бұрын

+Money Man I am working on it. Currently doing a lot of ANOVA, and Machine Learning videos.

@argentman88

8 жыл бұрын

Thanks for the quick reply! Looking forward to viewing it! Keep up the excellent work!!!

@RyanReddell

7 жыл бұрын

Couldn't you just use 12 periods per year instead of 4 or will that affect the data?

What if there is no particular Seasonality in the data?

@rdjalayer

9 жыл бұрын

Karthik Khanna no need to remove what is not there, go straight to trend estimation

@karthikkhanna1263

9 жыл бұрын

thank you!, anyway you took a long time to comment

What if there's no seasonality?

Why does I fall out in the notation from column G to H? Where does the irregular component go?

@rdjalayer

9 жыл бұрын

by averaging over the respective quarters we mitigate the effect of the Irregular component

@Canuckish

9 жыл бұрын

Jalayer Academy so this is Assm: E[e] = 0

@Canuckish

9 жыл бұрын

Jeff van Geete would it be appropriate to think of the difference between G and H as I(t)?

@rdjalayer

9 жыл бұрын

Jeff van Geete when we average all the seasonal components for each quarter we are in effect mitigating the irregular component and offering the average and the irregular-strip seasonal component