Lets Implement LSTM RNN Models For Univariate Time Series Forecasting- Deep Learning

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In this tutorial, we will explore how to develop a suite of different types of LSTM models for time series forecasting.

The models are demonstrated on small contrived time series problems intended to give the flavor of the type of time series problem being addressed. The chosen configuration of the models is arbitrary and not optimized for each problem; that was not the goal.
Thank you Jason
#TIMESERIESFORECASTING
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One of the finest explanations in youtube. Krish Naik is a legend

souravdey

Amazingly simplest way complex concept of LSTM explained ...you are getting many blessings from aspiring Data scientists . Thanks a lot

shekharkumar

I must say it is an amazing presentation on LSTM. Thanks a lot.

kvafsu

Krish sir i ever get a chance to thank you I will surely not gonna miss that chance you always save my ass from errors bugs and help me to understand in a better way

Hitesh-Salgotra

Great tutorial!
The n_features you talked about is the number of features in one time step. i.e. if x=[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], samples=4, time_steps=4, and n_features=3

advaitshirvaikar

I salute you sir for your explanation and choosing the concept.

helpyburhanuddin

Was waiting for this from long time. Please sir more videos on deep learning. Great explanation by the ways

BhupinderSingh-rgbe

Thank you explaining this in simple manner . Waiting for multivariate time series analysis

prakashdhakal

You must re-read Jason Brownlee' sblog in order not mixing terminoly !
Your example is univariate time serie nb_features = 1 and what you call nb-features (preparation of data) is the nb_step !

WahranRai

Hello Krish sir,
Your Machine learning playlist was great enough to clarify many doubts for someone like me who is aspiring to be data scientist.
Can you make playlist on time series analysis please! :)

Chandrashekhar-sw

Thank you for explaining it in such simple manner. Eagerly waiting for the multivariate Time Series analysis and it will be great if you can work on some live data and demo us with these concepts such as Stock price forecast, Cryptocurrency prediction or any other real-life usecase.

sidheswarpatra

Thank you for the explanation, really help me for complete my thesis

cahyoardhi

Great tutorial!
Please also make a tutorial on Multivariate Time series forecasting

kashifjavedlone

Krish a very nice and good explanations for the topic.Thanks for spending your time and helping.

rakeshyou

Thank you very much sir or this i was waiting for this so long.

adityataksande

Hi sir, eagerly waiting for the multivariate analysis video

premaldoshi

Thank you so much Sir, please also do a multivariate time series where there are few categorical and numerical features which do not depend on time.

I have one problem which I could not solve wherein the data was given for 24 days on hourly basis and prediction was supposed to be made for balance 6 - 7 days (hourly prediction) for each month.
it is like information of last week of each month for a span of 4 - 5 year is not present in train but present in test for which prediction has to be made.
I would appreciate if my comment catches your kind attention. Thanks for your continuous support and helping many aspiring Data Scientists out there.

sanjeevkumar

You are the type of person i wanna be. Great video krish love from Pakistan

kabeerjaffri

please make a video for multivariate time series forecasting....your explanation sir is awesome!

bnnibell_

Thank you so much for this, explanation was great and understood it very well. Keep up the good work.

sruthiparvatha

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