So, the forecast Column you added right beside Seasonal, Is that for those months or for 25th onwards? If not then should we just drag the forecast data from 24th onwards to forecast till 36?
Other Questions: 1) Can the smoothing constants be 0 and 1? Sometimes solver yields values 0 & 1 2) For Forecasting for the first 24 periods , you have always taken m =1, and the forecast is calculated by taking Level and Trend value of the previous year. Can 'm' be 2,3 etc. If we need to give more weightage to the data of the latest years, should we take 'm' always as 1 ? 3) For Forecasting for periods, t =25,26,27 etc should we use F(24+1), (F24+2), (F24+3) as Forecast formula, m = 1,2,3 ?
Thank for for the response. No, that makes perfect sense. I guess I'm just a little unsatisfied with carrying on the level and trend of time t for future projects with no variation. I also feel that the cycling of the seasonality values for t-L for anything beyond the period may be a little unreliable. Perhaps I need to look into a more advanced model to which may address trending the seasonality values as well? Would ARIMA be my next step from here?
This way how can I forecast for next 12 months as next 12 months there are no actual demand. So how can I get trend seasonality and forecast for next 12 months?
If I need to provide a 4 months forecast of the demand, what changes should I be making to the above? Would just taking M = 4 be sufficient? Once I reach the period 24, how do I forecast for all future periods until period 28? By replacing M by 4 would the rows for the period 21-24 hold the forecasted values for the period 25-28?
I'm forecasting for my company(8 yrs historical data), Periods, t =96. Seasonality s=12. Its better if we initiate Level by taking the average demand of the first 12 months & calculate seasonality index for 12 months.Taking 3 months avg, forecast for future months is stabilized, no seasonality left. For the forecast, for ex t =97,98 etc do I have to take F(96+1),F(96+2) for calculation of level, trend keeping m =1 or can m = 2,3 also ? More priority should be for the data of the latest years.
Hello, thank you for the excellent video, which I am using for my personal study. However, I am curious as to how you can use this forecast model to predict future behaviour? For example, if I have a series with a period of 7, how can I extend the forecast model beyond t+7? This is for a forecast of daily demand (with varying seasonality specific day) where I have months of data, but I cannot determine how to extend the model for a month of future data. Any advice would be very appreciated.
Thanks for the comment. You can adjust how many periods you forecast into the future in this technique. In this example, I forecasted one period into the future (i.e. m=1). I can however, easily adjust what value m takes in the formula. In practice you have to pick the appropriate rows in the spreadsheet. I know this is not easy to explain in words, but hopefully this makes sense.
ameya pradhan Someone plz correct me if I m wrong, I think this is the U stat they are talking about- docs.oracle.com/cd/E40248_01/epm.1112/cb_statistical/frameset.htm?ch07s02s03s04.html
here t=3 and m=1,means we are forecasting in period 3 for period 4 (3+1). in actual literature this notation is F(t,t+m), the inside braces is subscript.
Winters' method employs a level component, a trend component, and a seasonal component at each period. It uses three weights, or smoothing parameters, to update the components at each period. Initial values for the level and trend components are obtained from a linear regression on time. Initial values for the seasonal component are obtained from a dummy-variable regression using detrended data. The Winters' method smoothing equations are: · Additive model: Lt= a (Yt - St- p) + (1- a) [Lt-1 + Tt-1] Tt = g [Lt - Lt-1] + (1 - g)Tt-1 St = d (Yt - Lt) + (1 - d) St-p t = Lt-1 + Tt-1 + St-p · Multiplicative model: Lt = a (Yt / St-p) + (1-a) [Lt-1 + Tt-1] Tt = g [Lt - Lt-1] + (1 - g)Tt-1 St = d (Yt / Lt) + (1 - d) St-p t = (Lt-1 + Tt-1) St-p where · Lt is the level at time t · a is the weight for the level · Tt is the trend at time t · g is the weight for the trend · St is the seasonal component at time t · d is the weight for the seasonal component · p is the seasonal period · Yt is the data value at time t · t is the fitted value, or one-period-ahead forecast, at time t
He used only the first 3 month to prime the model because if you look at the seasonality pattern, the seasonal pattern/cycle repeats every 3 month. Usually speaking it is better to use more data to prime the model, first 2 - 3 seasonal cycles. I use the same model at work, however as far as I can tell this is one of the older Holt-Winter models with fewer variables. If you look up research online on this topic you will easy find some newer Holt-Winter models which factor in trend line variables as well random noise control and cyclical adjustments which are all necessary to minimize forecasting error in big and important data sets
Ive been spending so long trying to figure out how to initialize trend, finally find this video and then this dude just puts a guess in.... wtf
Welcome to University Lmao
that's more complicated than python....
This was good but when do you actually forecast?
So, the forecast Column you added right beside Seasonal, Is that for those months or for 25th onwards? If not then should we just drag the forecast data from 24th onwards to forecast till 36?
Sure, it can be used for any type of data that has trend and seasonality
Other Questions:
1) Can the smoothing constants be 0 and 1? Sometimes solver yields values 0 & 1
2) For Forecasting for the first 24 periods , you have always taken m =1, and the forecast is calculated by taking Level and Trend value of the previous year. Can 'm' be 2,3 etc. If we need to give more weightage to the data of the latest years, should we take 'm' always as 1 ?
3) For Forecasting for periods, t =25,26,27 etc should we use F(24+1), (F24+2), (F24+3) as Forecast formula, m = 1,2,3 ?
Thank for for the response.
No, that makes perfect sense. I guess I'm just a little unsatisfied with carrying on the level and trend of time t for future projects with no variation. I also feel that the cycling of the seasonality values for t-L for anything beyond the period may be a little unreliable. Perhaps I need to look into a more advanced model to which may address trending the seasonality values as well? Would ARIMA be my next step from here?
This way how can I forecast for next 12 months as next 12 months there are no actual demand. So how can I get trend seasonality and forecast for next 12 months?
Hi Fun Hitch! Have you resolved your problem? Because I am wondering the same thing as you now! 😅
how do you get trend "2". how its calculated.
Can you explain the trend column? What does the 2 mean?
If I need to provide a 4 months forecast of the demand, what changes should I be making to the above? Would just taking M = 4 be sufficient? Once I reach the period 24, how do I forecast for all future periods until period 28? By replacing M by 4 would the rows for the period 21-24 hold the forecasted values for the period 25-28?
Thank you for the tutorial.. it was very productive (expecially in 1080p rsrs)
I'm forecasting for my company(8 yrs historical data), Periods, t =96. Seasonality s=12. Its better if we initiate Level by taking the average demand of the first 12 months & calculate seasonality index for 12 months.Taking 3 months avg, forecast for future months is stabilized, no seasonality left. For the forecast, for ex t =97,98 etc do I have to take F(96+1),F(96+2) for calculation of level, trend keeping m =1 or can m = 2,3 also ? More priority should be for the data of the latest years.
Hello, thank you for the excellent video, which I am using for my personal study.
However, I am curious as to how you can use this forecast model to predict future behaviour?
For example, if I have a series with a period of 7, how can I extend the forecast model beyond t+7? This is for a forecast of daily demand (with varying seasonality specific day) where I have months of data, but I cannot determine how to extend the model for a month of future data.
Any advice would be very appreciated.
Shut up
Thanks for the comment. You can adjust how many periods you forecast into the future in this technique. In this example, I forecasted one period into the future (i.e. m=1). I can however, easily adjust what value m takes in the formula. In practice you have to pick the appropriate rows in the spreadsheet. I know this is not easy to explain in words, but hopefully this makes sense.
Hi great video thank you very much sir. However, I must know how do you calculate the u-stat in microsoft excel?
Hi Guys, May I know how to compute the 80% and 95% forecast intervals in this example? thanks.
Can anybody Please explain how to calculate the U-Stat?
Bump! I would like to know too!
lariksonfar
basically (current demand period - previous demand)^2
ameya pradhan Someone plz correct me if I m wrong, I think this is the U stat they are talking about- docs.oracle.com/cd/E40248_01/epm.1112/cb_statistical/frameset.htm?ch07s02s03s04.html
ameya pradhan =sqrt(sum(sq error)/sum(u-stat)), so in this case =sqrt(sum(K8:K25)/sum(L8:L25))
at 7:56 where you are calculating t+m-s you say t+m =4. But why? t=4 m=1 s=3.
So 4+1-3=2. What am I missing here? Thanks in advance.
here t=3 and m=1,means we are forecasting in period 3 for period 4 (3+1). in actual literature this notation is F(t,t+m), the inside braces is subscript.
Winters' method employs a level component, a trend component, and a seasonal component at each period. It uses three weights, or smoothing parameters, to update the components at each period. Initial values for the level and trend components are obtained from a linear regression on time. Initial values for the seasonal component are obtained from a dummy-variable regression using detrended data. The Winters' method smoothing equations are:
· Additive model:
Lt= a (Yt - St- p) + (1- a) [Lt-1 + Tt-1]
Tt = g [Lt - Lt-1] + (1 - g)Tt-1
St = d (Yt - Lt) + (1 - d) St-p
t = Lt-1 + Tt-1 + St-p
· Multiplicative model:
Lt = a (Yt / St-p) + (1-a) [Lt-1 + Tt-1]
Tt = g [Lt - Lt-1] + (1 - g)Tt-1
St = d (Yt / Lt) + (1 - d) St-p
t = (Lt-1 + Tt-1) St-p
where
· Lt is the level at time t
· a is the weight for the level
· Tt is the trend at time t
· g is the weight for the trend
· St is the seasonal component at time t
· d is the weight for the seasonal component
· p is the seasonal period
· Yt is the data value at time t
· t is the fitted value, or one-period-ahead forecast, at time t
how to calculate u-stat ? is there any formula ? or any built-in formula at excel?
Hi,
Do you have a copy of this spreadsheet to have a look at the background formuli?
Thanks,
Gary
how the term u initiate trend & level?
How did you calculate the U-Stat?
How to forecast for 25 and beyond?
nrajendra It's a very late reply...but here's what I came up with: (D25+1*E25)*F14 or (Level + 1*Trend)*Seasonal
How to calculate the initial trend?
How did he calculated the initial trend?
how to forcast days wise sales
You are taking 12 month, Why s=3?, shouldnt be s=12? Why do you calculate the estacionality taken 3 period in an annual series?
He used only the first 3 month to prime the model because if you look at the seasonality pattern, the seasonal pattern/cycle repeats every 3 month. Usually speaking it is better to use more data to prime the model, first 2 - 3 seasonal cycles. I use the same model at work, however as far as I can tell this is one of the older Holt-Winter models with fewer variables. If you look up research online on this topic you will easy find some newer Holt-Winter models which factor in trend line variables as well random noise control and cyclical adjustments which are all necessary to minimize forecasting error in big and important data sets
Dmytrii B Hi. do you have a link that shows a practical example?
will u post the excel file?:)
See his other videos, he explains in some of them.
hi does anyone have the excel file? thanks
that's more complicated than python....
How did you calculate the U-Stat?
search for Theil's U statistic.