Random Walk in Time Series Analysis | Forecasting | Statistical Analytics

#randomwalk #unitroottest #timeseries #paneldata #arima #forecasting #stock
In this video, we discuss about what is a Random walk process in Time Series ANalysis. We also discuss Unit root tests to detect randomwalk.
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Complete Data Science Course : bit.ly/34Sucmb

[@pravinshende.DataScientist]

I am new to this but after some days this will become very simmple for me .. My strategt is frist give importance to the theory learn it and then go for the coding ....

[@surajrout2482]

Thank you

[@raidjameel7513]

Non of the people in Utube r able to explain, present and talk about Random Walks Theory Origin, and its importance and applications, in real world, in simple easy way(s) to understand.

[@hossainjoki3146]

I found some statement a bit confusing! Once you are telling you can not do anything if the data is a random walk, just the current value is the best prediction. Again you are telling you can make it stationary by taking the first difference!

[@alikaraca163]

Clearly, there is. So far I got from his videos is a random walk and white noise are the same processes and once when you see the features there is nothing that can be done for future forecasting (non-stationary). However, there might be some different types of non-stationary series, and when differencing it, you can get stationary series. (transformation). For instance, trend and seasonality make series non-stationary and can be made stationary by differencing. This is what got.

[@babua8175]

in one place you said uniroot is @=1 and other place you said uniroot system is @=0.

[@SESHUNITR]

Explanation seems to be unclear

[@behzadnourani3195]

honestly, not a good presentation and explanation, without any insights. just jumping from one definition to another ...