I really get clear and encourage to continue in my BMS specialized subject of OPERATIONS MANAGEMENT. Thank you and I wish to continue getting more on "Time Series Analysis.
This example is very god for my purposes of planningthe next week (Mon - Fri) volume of packages my company will get. Will comment the results after applying this concept
I thanks you for this indepth explanation guyz . i grasp the time series concept by just watching this video , i was really struggling to understand it but you guys i thank you🤗🤗🤗
i have some exercises i did it but i don't know if that result correct or not so the exercises are ; exercise 1: Forecast future demand using the three forecasting methods: Month1: 630 Month2: 650 Month3: 720 Month 4: 700 a. 3 months moving average b. 3 months weighed moving average knowing that weight for the 3 first months are 0.2, 0.3, and 0.5 respectively. c. Exponential smoothing knowing that: Alpha = 0.1 exercise 2 Exercise5: The demand for a product in each of the last five months is shown below. Month 1 2 3 4 5 Demand ('00s) 13 17 19 23 24 • Use a two month moving average to generate a forecast for demand in month 6. • Apply exponential smoothing with a smoothing constant of 0.9 to generate a forecast for demand for demand in month 6. exercise 3 You are a 5 stars hotel and have had many negative reviews on trip advisor. Most of your visitors check the website before reservation. The following comments were shared. Define the problems and try to solve them using the Ishikawa diagram “Food options were very limited. Makeshift fitness center was not to justify the price and stars” “The staff is not very keen to help during check-out I met with one receptionist was Stone faced” “I was not informed in advance that hotel is under construction. Spa, pool gym and some Restaurants were inaccessible.” “Very bad concierge and service ... it’s very expansive for the service they provide as 5 star hotel “The property was significantly older than the photographs used to market it and the rooms show their age. In our room, there were carpet stains and broken furniture. Also, the cleanliness of the sheets left something to be desired. Upon our inspection of the bed, we found matted hair in the sheets”
This is really good. Teaching with so much of patience. Could you release such lessons for decomposition methods of time series ? (additive and multiplicative). Very clearly explained.
hi, sir, because this set of data is seasonal, we must first de-seasonalize the demand data to get a new set of data, then build the regression function on the de-seasonationalized data to forecast future periods demand ( level + trend ), then times the de-seasonalized forecast with seasonal factor to get the required the forecast .
sir i got confused when u said that the value of y we multiply by the seasonal index of Q4 which lately at the table was 130,80 then here u said we multiply by 130,84 where do this 4 comes from ,the tution is much nice and so fantastic and easy to be understood
Nice lecture indeed.I haven’t seen ever this kind of explanation. Easy to understand. Can you please give me some examples? where can we apply this in real time?
I had a negative attitude on this course but this video has proven that I was wrong all along. THANK YOU SO MUCH.
Wao fantastic indeed t is the best explanation i have ever had on this topic. Thank you Sir
I really get clear and encourage to continue in my BMS specialized subject of OPERATIONS MANAGEMENT. Thank you and I wish to continue getting more on "Time Series Analysis.
Fantastic tutorial, so simple and easy to understand. Thank you
Thank you so much for this in-depth explanation. I understand the concepts better after watching this video than I did when I learned it in campus.
Just made my day. Time Series made simple. Very clear and illustrative examples.
Very lucidly explained. A couple of listenings will drive home the method fully.
Wow! This is the best explanations so far in time series analysis. God bless you real good, sir.
can we put our hands together for the good teacher. you are understable
This example has given me a clear understanding of time series analysis and in facts am appreciating quantitative method.God bless you
This example is very god for my purposes of planningthe next week (Mon - Fri) volume of packages my company will get. Will comment the results after applying this concept
You didnt commented 😁
The way you explained it was extremely easy Sir. Thank you very much.
2020 sitting for my QA exams amidst the Corona pandemic and God sent me here...Thank you sir.
are you tired of learning through zoom
Thank you for this particular lecture Sir.Its amazing and was easily understood.
I thanks you for this indepth explanation guyz . i grasp the time series concept by just watching this video , i was really struggling to understand it but you guys i thank you🤗🤗🤗
This is very helpful I understand it clearly now. I will share this video with my MBA group members
Excellent presentation. Very clearly stated.
Thank you very much, 2019 and we are still watching...
Educational video i have learned alot from it keep up the good work may God bless you
THIS WAS SOO EASY I NOW UNDERSTAND TIME SERIES BETTER. PLEASE CAN YOU RELEASE A TUTORIAL ON ARIMA
Thank you so much. The explanation is easy to follow and understand.
Darn Adhir, You have helped me a lot as I type. Very vivid and concise. Thanks!!!
I missed a lecture on this topic, and this tutorial has been very helpful. Thank you sir.
Very informative ! Thanks for the Wonderful Video !!
Oh wow I've been stuck to the similar question in regression part. It has helped me so much thank you
This has been helpful ..I have an exam in the next two weeks and this has been the best explanation
Very good tutorial, you are a good teacher and the lessons is very easy to understand.
Thank you so much
Thank you so much for this explanation...you made it really easy
Thank you
wow! very good teacher with proper explanation
Thanks alot.... I didn't understand my teacher....you make it seems so easy and I love it
this was so clear and instructive.
Great work done. Easy to understand and go by. Thumbs up
The best explanation ever for time series analysis
Thank you so much for simplifying time series analysis.
This is easy to understand. Thank you so much for this tutorial.
Thank you ,what a great Teacher,I really enjoyed it
Great lesson, well explained. ICSAZ student Zimbabwe.
2020 and still watching
2021
2022 😁
2023😁
2024😂
Thanks this has helped me a lot in my exams as a student
excellent teaching sir. This has been really helpful thank you.
Woow good work 🎉🎉🎉🎉🎉❤
Finally, I got it here. Great work done.
Section made clear. Nice lecture indeed. Thank you very much sir.
Great tutorial and straight to the point.
Thankyou
Thank you for making it so simple with good explanation of the subjects ...kudos to you
thank you very much! So helpful and clear. You made it easy thanks again!
This so perfect and explanatory,am so greatful, I've been struggling with time series for sometime now. Thanks a whole lot
Very helpful. Thank you...!!
Excellent I have found it to be very lucidly explained example on time series. Congratulations Adhir.
You explained this perfectly. Thank you so much!
i have some exercises i did it but i don't know if that result correct or not so the exercises are ;
exercise 1:
Forecast future demand using the three forecasting methods:
Month1: 630 Month2: 650 Month3: 720 Month 4: 700
a. 3 months moving average
b. 3 months weighed moving average knowing that weight for the 3 first months are 0.2, 0.3, and 0.5 respectively.
c. Exponential smoothing knowing that: Alpha = 0.1
exercise 2
Exercise5:
The demand for a product in each of the last five months is shown below.
Month 1 2 3 4 5
Demand ('00s) 13 17 19 23 24
• Use a two month moving average to generate a forecast for demand in month 6.
• Apply exponential smoothing with a smoothing constant of 0.9 to generate a forecast for demand for demand in month 6.
exercise 3
You are a 5 stars hotel and have had many negative reviews on trip advisor. Most of your visitors check the website before reservation. The following comments were shared. Define the problems and try to solve them using the Ishikawa diagram
“Food options were very limited. Makeshift fitness center was not to justify the price and stars”
“The staff is not very keen to help during check-out I met with one receptionist was Stone faced”
“I was not informed in advance that hotel is under construction. Spa, pool gym and some Restaurants were inaccessible.”
“Very bad concierge and service ... it’s very expansive for the service they provide as 5 star hotel
“The property was significantly older than the photographs used to market it and the rooms show their age. In our room, there were carpet stains and broken furniture. Also, the cleanliness of the sheets left something to be desired. Upon our inspection of the bed, we found matted hair in the sheets”
Great explanation.
i'm about to study time series analysis soon and this is the best overview i have ever seen.
thank you so much!
Thank you very much sir. This tutorial has brought me into the light
much appreciated good work. you have help many
well explained thank you 😇
thanks for such a great explaination
This wonderful and easy to understand, thanks
Wow..!!! Amazing lesson... thank you so much.!
Very well explained, to the point with good examples and easy to follow. Thanks!
wow that was very awesome... I couldn't stop smiling. Thank you so much sir!
This is really good. Teaching with so much of patience. Could you release such lessons for decomposition methods of time series ? (additive and multiplicative). Very clearly explained.
Hello
its so helpful ,please provide more vedios.u ar simply the best guys
Excellent... Thankyou very much!
Very clear and understood ,,,, great
Thank you sir, saved me a great deal.
You teach so well sir!
Thank you very much Sir. Appreciating your valuable introduction !!!
great video, really helpful
THIS VIDEO HAS BEEN VERY HELPFUL TO ME GOD BLESS YOU
Excelent!. Great tutorial on Time Series with a practical, real world example.
U r great sir
Very enlightening
Very well explained, thanks a ton
Thank you for the lesson
Well illustrated Sir. Adhir Hurjunlal. The knowledge is still helpful six years later.
hn
Very well explained. Thank you.
Very clear, thank you
hi, sir, because this set of data is seasonal, we must first de-seasonalize the demand data to get a new set of data, then build the regression function on the de-seasonationalized data to forecast future periods demand ( level + trend ), then times the de-seasonalized forecast with seasonal factor to get the required the forecast .
Very Good, you have made my life easy
tutorial approved by the comic world,,, thank you Adhir Hurjulal
WHO ELSE IS WITH ME
Wonderful 😊 still watching up to now
Thank you so much this has been very helpful. just made me feel like time series is super easy after I have suffered so much
Well explained
nice to the point concise lecture
Well done, definitely make more content. Clear, easy to follow, and informative.
Thank you very much. It helps a lot.
sir i got confused when u said that the value of y we multiply by the seasonal index of Q4 which lately at the table was 130,80 then here u said we multiply by 130,84 where do this 4 comes from ,the tution is much nice and so fantastic and easy to be understood
Nice lecture indeed.I haven’t seen ever this kind of explanation. Easy to understand. Can you please give me some examples? where can we apply this in real time?
Hi thank you for such a wonderful video. Could you please do a practical example of ARIMA
This is awesome.
fantastic tutorial, thanks.
This videos helped me so much
well taught and explained
Very good explanation 👍
Very Very good! Big thanks
Thank you for this video it really help
This tutorial is very helpful
THANKS FOR THAT GREAT LECTURE
G R A C I A S mil desde COLOMBIA. It works perfectly with animal production. I do something similar using percentage. Thanks in advance!!!.