Smoothing 4: Simple exponential smoothing (SES)

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Simple exponential smoothing is a popular data-driven method for forecasting series with no trend and no seasonality.

This video supports the textbook Practical Time Series Forecasting.

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Finally!!! This lecturer is highly technical, but does a superb job of explaining everything!!!

FlareSoul
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You are more than I could imagine, simple, clear, short and also digged deep.

ahmedashry
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Your English is so perfect, and your voice is so clear, I could listen to your training forever...The way you communicate the topic is superb. As a beginner in the topic, I understood right away.

cananakca
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Thanks Galit for such a clear explanation ! The visuals - formulae, example and ur explanation went really well together and helped to understand the concept easily!!

skrishnakumar
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At 3:35 L(t) = ... should be L(t+1) = ... .Do you agree? (nice lecture by the way)

VideosEngracadosBR
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It's really a great series of videos explaining time series analysis!!

xinyichen
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This is a fantastic video for studying time series! I have one small question: at 6:18 the teacher explained that trailing MA with window W almost equals to SES with alpha = 2/(W+1). I did not quite understand this part. Could anyone please explain a bit on how the conclusion is derived? Thanks!

xinpang
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By Lt and Lt-1, I assume this is the same as notation in your book that uses the Ft and Ft-1 for the forecast value?

DogsAndDataRus
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But you only gave an example for SES where you used the last forecast value, alpha, and the error. Where is the example on a weighted average for a series of past values?

PinkFloydTheDarkSide
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What if the time deltas between samples are not equal?

SaidMetiche-qyhb
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So how exactly could you forecast the values beyond the 1 period immediately succeeding the last available historical data?

glennsugana
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This is fantastic - thank you, Galit!

matthewhoag
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Thanks for this great effort. Appreciated!

yousefhassan
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although back in 2016, but super explanation. sharp and crystal !

HazemAzim
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how beautiful and easy explain. Thanks

davidkim
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