If only professors could be as clear as this video as well as Joshua's other videos. Thank you so much sir!

Sir, I'm not exaggerating when I say you've saved my life. God bless you

Sir, I'm not exaggerating when I say you've saved my life. God bless you You Give me So much better than my teacher's explanation. Thanks! a lot

Thank you. You literally explained something that my professor couldn't for 2 straight hours. Thanks a lot.

You are a better Professor than MY professor !!!! more clear and easy to understand!!! 100% GOOD for international students!

Saved me 5 hours of reading a complicated book. Thanks!

thank you sir, it took me 5 min to understand it clearly, u deserve everything in this world 😭🙌🏻

You have saved me! I've been stuck on a problem like this for hours!

Thank you! I was so confused by this in my textbook. You broke it down to where I can understand the material!

Now I can pass my exam! Phew... Thank you for this. Very clear

The 4 weeks percentages were already convent to a percentage, that's why it adds up to 1. That's also why you divide the WMA by 5 for week 2. 3(x)/5 + 2(y)/5 = (3x+2y)/5 I think the important part to remember is to convert to percentages

Legit needed this vid. Thank you. If it wasn't for this vid I'd be stressed as hell.

Out class video... My exam will be conducted in a few hours I'm seeing this video all concept cleared. Thank you. God bless you dear. More n more shared in my class mates Whatsapp group

Very helpful video. This channel must be located by students.

You're a legend! I wish my professor was like this :(

i just fall in love with your method of explaining🙈🙈🙈

Awesome job of breaking it down!

this was very helpful before I had to take an exam. Thank you!

you are a great teacher sir everyone is a teacher but everyone is not have a Idea how to teach you teach very well sir

Thank you man tomorrow is my exam. It helped me. Great video.

Given the following data, use exponential smoothing with α = 0.2 to develop a demand forecast for period 7. (Forecast for Period 1=10).

Thank you sir! Your videos are clear and I always leave smarter than when I first arrived!

This was very helpful. Thank you.

Excellent explanation!

thank you for your deeply help.

I would suggest changing the values for weeks to a date structure. It makes it easier to follow. The term "next most recent" is an odd way to say the previous week

Very good explanation

You saved me. Merci beaucoup!

Correct me if I am wrong but for Forecast 5, the Numerator should read 3(45) + 2 (40), not 3 (45) + 2 (44) since we are using the last two most recent Sales (45 and 40). The answer 43 is correct though. Thanks by the way, you explain it so clearly!

Thank you! 1. Is weighted moving average, or moving average really a forecasting method? Or is it a way of smoothing the data? (I am trying to learn and understand. ) 2. How do you determine which weight value to use? Thank you for the insight.

Thanks for this simple and straightforward explanation. I have a question on when do we use MSE vs MAE for error calculation

thank you so much this was really helpful, it was loud and clear...

Your videos are amazing and really easy to understand, do you have anything about Simple Linear Regression?

That was very comprehensible. Like it.

how simple and easy!!! Thank you

Thank you very much, Sir for explaining well.

May the force be with you Joshua

Thank you for this, Sir!❤️

how to decide of their weights ??

Big thanks you are GREAT , VOICE, VERBAL , SMOOTH EXPLANATION, THANK YOU :)

thank you for the explanation. this was soo easy to understand.

you are AMAZING

Mister my question the instruction only weight moving average approch by your given example how to calculate?

Thank you sir!

Well Explained, if i have the Data and i should put the weight values how i can calculate them ! i just have Data for sales last year and want to use this method it's applicable and how ! thank you very much for your support

Can we using the method for seasonal data where the irregularity can be extreme? For example, can we know that next week the sales would be 60, which is very different than the rest of your data?

Man, good job. It was helpful.

Thank you so much I understand everything keep up the good work 👍

if I got my next job, I must thank you

Hello Joshua,I would like to thank you for wonderful explanation.i have a question will the smaller value of m will give good forecast if so then how.

Thank you !

thank you very much for this video, why is 45 the most recent value to start?

#Weighted Moving Average 4WMA weight = [0.4, 0.3, 0.2, 0.1] sales = [39, 44, 40, 45, 38, 43, 39] calc = lambda x: 0 if x < len(weight) else sales[x-1]*weight[0] + sales[x-2]*weight[1] + sales[x-3]*weight[2] + sales[x-4]*weight[3] forecast = [ calc(x) for x in range(0, len(sales) ) ] print(forecast)

Very incredible, wish you include more topic in that video lecture

well explained and very helpful, Thank you so much Joshua

i love this dude

Great explanation!! Thank you so much :)

really helped well sir thank you t__________t

thank you for this amazing explanations 👌

Man, during 3:42, I think that something went wrong. So, for week 5 we should have to calculate (3*45+2*40)/5. 2*40 not 44. Because as seen all other calculations we have done it the first 2 CONSEQUENCE numbers from back to front.

Thank you, that was useful for my study!

What is the benefit of taking Moving Averages? How can we interpret MAD?

Very good explanation, thanks

How to find exp entail smoothing regression analysis, Markov chain, simulation. Thank you

Thank you very much

Thank you. Which formula can I use in predicting the overhead budget. Thanks again

Great vid. Thank you so much!

Joshua you are the man

thanks! i Got great ideas for this videos!

That Really helped alot sir! Thankew

so finally there are 2 types of method if we got value 1 by calculating all given weight value so we have to have solve from 1st method if we are getting another value like 5 or 7 or 10 by calcuting so we have to do by the second method

thank you for this

Thank you for thisssss 🥰🥰

Very helpful, thank you.

Thank you sir..❤️

How do you differentiate Mean absolute deviation vs Median absolute deviation which is also short for MAD

if i increase number of weeks then error will decrease or increase. if error is increase as shown in ur example that 2wma give better smoothing than 4 wma then it means as number of periods increases the smoothing error get increased.

LEGEND!

Hello.. The video is great explained very clearly But I had a doubt, when "we assume the weights of different values the sum of their weights should be equal to 1", Here It is satisfied for 4week but not in case of 2week??

Thanks forgot it 👍🏼

what if the ( 4 weeks ) is not given, the question only says find the forecast for a specific month? how can i solve ?

How about there is no given weight average but we need to calculate the three months and six montsh weighted moving averages?. Can someone help me/enlighten me please. THANK YOU

ur great.thanks a lot . awesome explanation

How can we know what weights should be used?

Thanks a lot 😊

great with the second tutorial.

Very good video thank you

What if we want to forecast more than one values in future. For example 21 future values in future? considering we have enough past data.

Thanks for this 😊

hi joshua could you further evaluate for Normalised weighted Root Mean Squared logarithmatic Error,please

How do you know which weight values to use?

thank youu Sir ❤️

HOW DO I CALCULATE THE FORECAST GIVEN 3 WEIGHTS ONLY?

Why use weights 3 and 2 at 2WMA instead of using weights 2 and 1?

Why can forecast be called a forward-looking variance analysis?

Please Josh how would interpret the 2.92 result?

Thank you so much for the videos. But I’m wondering if we could calculate week 8-10? how could I do that?

Anyone know how it work on "optimum weighted moving avarage". There is no weights?? 🤔

How can you calculate the sales for week 9 and 10.

If there three weights in question but two of them are given. How can I calculate the third weight, since it wasn't given. Thanks