amazing content> very superb man i understood each and everything. For this shit i am paying 4 lakh rupees in a university in United states but cannot understand anything here. But this guy made it so simple. Thanks man. Reallly appreciate.
Thanks for your videos! I'm new to time series forecasting and your content gives me a good overview. I noticed though while reading more on the topic that you might have mixed up first order differencing with lag difference. What you describe ( Y(t) - Y(t-2) ) seems to be called a lag-2 difference and apparently order is how many times you do the whole process.
I understood the differencing part, but I have a question. If we take the differencing and reduce the scale of data from 400-500 to 0.1-0.5, wont we have trouble scaling the output later ? and if we take the differencing from the data to make it stationary doesnt it mean that we are changing the nature of the data and instead we can use a better model that can work with the current state of data ?
Dhanyavaad. As a data scientist I found this was very helpful
amazing content> very superb man i understood each and everything. For this shit i am paying 4 lakh rupees in a university in United states but cannot understand anything here. But this guy made it so simple. Thanks man. Reallly appreciate.
Love the playlist on Time Series Forecasting. Hope you upload more videos.
Thanks, and yes will definetly put out more!
Thanks for your videos! I'm new to time series forecasting and your content gives me a good overview. I noticed though while reading more on the topic that you might have mixed up first order differencing with lag difference. What you describe ( Y(t) - Y(t-2) ) seems to be called a lag-2 difference and apparently order is how many times you do the whole process.
its really good vedio to understand concepts,good work
Very helpful video ❤
very well explained keep it up.
Nice explanation. Your channel is underrated
Thanks for the video !! superb explanation
bhai apne itna accha knowledge kaha se liya ??... thanks for passing it brother
GOD bless you
This video is worth watching....!
Can you please make a video on Augmented Dickey Fuller test.
please do make a video in mathematical calculation in both statistical tests for stationarity
Amazing Explanation Brother....
Well explained. Looking forward to more content.
Thanks!
Thankyou for such simple explanation! Great!
Glad it helped!
Mate this is so informative!!!
You are a Rockstar ❤️
Great explanation brother
in python arima there is auto differencing? we don't need to fit model with this differenced time series? just select 'I' value? second param
Superb explaination
I understood the differencing part, but I have a question. If we take the differencing and reduce the scale of data from 400-500 to 0.1-0.5, wont we have trouble scaling the output later ? and if we take the differencing from the data to make it stationary doesnt it mean that we are changing the nature of the data and instead we can use a better model that can work with the current state of data ?
Can you share example of forecasting using hybrid models?
Okay so what is the interpretation of the final result vs the exponential curve?
Dude.... Thankyouuuuu soo much buddy
Hello Nachiketa Can you just tell me if there is any videos regarding the theory content on this particular topic...
very nice explanation
i wanted to know can i do time series analysis on yearwise median income of men in any particular country???
You are amazing sir !
Thanks!
how can we get the actual predicted values back when converted data to stationary?
do you know answer now?)) i have the same quastion beacause this stationarity convertation is not work for me) same mape
are you from VIT Vellore? the background in the video looks like vit hostel
superb bro
Please make a video for statistical test
Hey, i liked your video, But can you please share the codes for the log operation, etc. Thank you.
If the time series is stationary, does that mean that there is no white noise?
That means it doesnt have trend,seasonality,cyclicality
That is mean and variance are constant.. white noise is when the distribution is stationary mean=0 , constant variance, plus autocorrelation is 0
HI, can you also share these ppts please? :/
thank you
Nicee
kudos!
why there should be no seasonality
👍
dude is 15?