Forecasting Methods made simple - Exponential Smoothing 

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Thanks for dropping the comment. Extremely happy that the videos helped you learn better. Do share the videos with your friends and also have a look at other videos on my channel.

Thanks a lot for your comments Swamy.

very nice for understanding alfa ..........

Muhamad Fazrral thanks. Have a look at all the videos in this series and let me know your views

Hi Mike, it is exactly the same formula and the terms are rearranged. Try opening the parenthesis in my formula and rearranging and you will have the same (1-alpha) based formula. If it does not work, let me know and I will try to help you.

You can use the solver option in excel which gives the most optimum value for smoothing

Don't listen to this idiot, he doesn't know shit.

You can take any value from 0 to 1

Generally no. Since accidents are generally dependent on some factor, poisson regression based methods work better.

Forecast your past data with different values of alpha. Select that value of alpha with the least error in predicting the past. Look here for ways to measure the forecasting error. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-WYBK4n75k_8.html

+Subhashree Prasad: These methods have an assumption that the past pattern will repeat in the future.I don't think this applies to financial markets and hence they cannot be used. I am assuming that there would be different methods to be used for yearly, daily and hourly predictions.

This method applies only to stationary series. Stock market is not a stationary series. Stock market follows random walk pattern.

You can find details of this method in any text book on Operations Management.

Can you explain your question in detail please?

2 forecast means 2 answer.. I understand from my teacher..

Exponential Smoothing and Moving averages can only be used to forecast the immediate next period. The idea is that the 'noise' or random variations in demand get smoothened by these methods. Thus as an assumption we are forced to believe that the predicted demand will be constant for all the periods in the future. If you use trend lines you can more easily forecast the future. Two things, One - the more you extrapolate the future, the more will be the inaccuracy of any model. Two - Choose the right method according to the type of variation you have in the concerned product.

Thanks Piyush

would you give some example from the real world that really work, but with critical minimum of tests. 100+ for example that give the percent of accuracy. For example, if we have $/€ price for last 40 days and try to predict next business day.

Please see the answer to your previous question. Once you know that a certain alpha is good for a certain data, we can use the method shown to forecast for the next month.

All supply chain and forecasting books should have the exact same formula.

I don't think so, since we can't have a specific goal for alpha. However, you can definitely use Excel Solver and select the alpha value that minimizes the error.

@@piyushashah1 I've never tried excel solver before.. anyways thanks for the reply brother 😁😁

@@sumukhhegde6677 so guess this is your chance to learn it

All these quantitative methods need past data to assess their suitability. And that is why these methods are often criticized as 'driving forward by looking at the rear view mirror'.Yet, the methods are about driving forward - and hence 'fore'cast. Let me know if you need more help on this topic and learn more.

@@piyushashah1 The column F shows the past, not the future. Why you call this method forecast?

Alpha is the degree of forecast sensitivity to past demand. So, if alpha is zero, forecast will not be affected at all by previous demand. And, if alpha is one, forecast for next month will equal to demand of previous month. In the video, as the alpha increases, the forecasts tend to get smoother. Check the graph displayed for this .

Piyush Shah thank you.. how to calculate degree of forecast sensitivity.? Please advise

Am not sure if that can be calculated. The idea is based on the forecasters belief of how much weight to assign to more immediate periods. If she wants to assign higher weight to immediate periods, the sensitivity needs to be higher for the immediate periods and the alpha thus has to be higher.

Well, time series method is about using historical data to forecast, and there is no doubt that with the correct formula, anyone can do it. Exponential smoothing method uses the data of actual and forecast of the previous month to generate the forecast of the current month - thats just the way it is.

@@piyushashah1 And what if you need to do forecast for whole next year, from this moment? But suppose you have only initial actual demand. What would be your actual demand for April, for example?

@@lukaivezic3924 Typically, more past data leads to better future forecasts. If you have one period data, these methods will then say that all future forecast are same as the one data point. So, if you just have one period past data, these methods may not work.

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