Classical Time Series Models AR,MA,ARMA,ARIMA - Understanding time series models in python

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Classical Time Series Models AR,MA,ARMA,ARIMA - Understanding time series models in python
#ClassicalTimeSeriesModel #UnfoldDataScience

Hello Guys,
My name is Aman and I am a data scientist.

About this video:
In this video, I explain about classical time series model Autoregressive model, moving average model, auto regressive moving average model, auto regressive integrated moving average. I explain how to work with different time series models using python. Below topics are explained well in this video:
1. Auto regressive model in python
2. Moving average model in python
3. Auto regressive moving average model in python
4. Auto regressive integrated moving average model in python
5. Working with Time series model in python

About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.

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Great Job explaining this! This channel is going to get a lot of views as soon as people start noticing it. It is very detailed. Just one suggestion - Please make sure to name the playlists in your description as otherwise it's a bit hard to find when you ask us to refer to one of your older videos.

cmpunk
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Great Video ..How to check the model performance and is there any variation limit or ranges for p, q, d

kalyankumar
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hi sir can you please make a video on how to be competitive in Data Science career for long term and what are the best practices to be superior in this domain?

sanyamsingh
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Nice. Do you know how to deploy a time series model using any web framework or cloud?

sriadityab
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Can you please make video on how to select proper d value, as trend their so how to decide proper d value what parameter take in count ?

rutvikjaiswal
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When I try to predict the future data it declare KeyError: 'The `start` argument could not be matched to a location related to the index of the data.'
How to fix it?

akhmadmasyudi
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Where we can find the notebook? Pls provide the link of notebook that would be very helpful

nilamsahu
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Sir the video was very informative but how PACF value to be taken was unclear

lancelotdsouza
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how to transform the data non stationary to stationary

Bro_call_me_Dude