Time Series Analysis and Forecasting - Forecasting Sales in Python - ARIMA, AR, MA models theory
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Published 2019-11-23
In this time series analysis and forecasting video tutorial I have talked about how you can forecast sales data in python. We’ll see the relevant libraries like pandas, matlotlib, stattools used in this project tutorial for data science and finally how we can compare naïve time series model, ARIMA model and hyperparamter tuned model to determine which model is working fine.
Also I have talked about various interview questions and tips and tricks related to time series theory and python code which can be asked in an interview or in a normal business scenario.
Dataset – groups.google.com/forum/#!forum/analytics_tutorial…
Jupyter notbebook used in this tutorial – www.instamojo.com/abhishek_agarrwal/time-series-fo…
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Thank you
Abhishek Agarrwal
All Comments (21)
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Extremely nice video. The way you explain the things, is too good. You don't leave behind any doubt about the things that comes in each programming statement. Thank you very much.
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FANTASTIC! Well explained for the beginners like me to follow and understand. Thank You.
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I have watched many youtube videos for time series but I really feel like this one helped me truly understand how to do it. thank you. My recommendation is that when you say " I covered this in my previous video" please tell us the name or title the videos in a way where we can follow a progression. Thank you so much for posting this.
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The way you explain is very simple and clear!! 💯
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Very insightful video sir...thank you!
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I really enjoyed watching your video. It clarified so many simple foundational concepts to me while learning through time series analysis. Thanks
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Amazing lecture! Many thanks 🙏
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nicely explained, could you also extend this to explain how to check/ test for various components presents in a time series data, and add more videos to build upon the modelling to find best time series forecasting for the shampoo data. Thanks in advance 👍
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very well explained thank you
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good video thx.. to answer one of your questions median is preferred when the data distribution is skewed and mean when its symmetrical
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The mean can be used to get an overall idea, or picture, of the data set. Mean is best used for a data set with numbers that are close together. The median can be used to get an idea of what values fall above the midpoint and what values fall below the midpoint.
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Very good course 👍 👌 😊
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You absolutely have no idea how many abysmal videos I had to watch before I found yours. Thank you for your sharing
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good explanation ; @6:10 "Kepted" lol
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Thank you
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Hi Abhishek, thank you very much for the clear explanation, Can you please provide the link or explain the python implementation for "Multivariate Time series" problem.
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Thank you a lot for this video but I have a question that comes to mind, why don't you take the sqr for the MSE in the iteration (for loop)?
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Hi.. When I give (3,2,3)..i am getting np. Sqrt(mean_squared_error(shampoo_test, shampoo_forecast)) as 75..can u check?
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Hi Sir, What approach we should follow when the target variable is following sigmoid or logistic or S curve with respect to time. Shall we still apply Time Series? If we can which algorithm we should chose as it has multiple variables affecting target variable?
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your model and teaching technique is awesome. I tried running model but in loop syntax, it showing me a syntax error. I was practicing on real data. Can you please help ( for p in p_values .....)
