Applied Time Series Analysis in Python
Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis
4.70 (785 reviews)
3,190
students
7 hours
content
Jul 2022
last update
$69.99
regular price
What you will learn
Descriptive vs inferential statistics
Random walk model
Moving average model
Autoregression
ACF and PACF
Stationarity
ARIMA, SARIMA, SARIMAX
VAR, VARMA, VARMAX
Apply deep learning for time series analysis with Tensorflow
Linear models, DNN, LSTM, CNN, ResNet
Automate time series analysis with Prophet
Why take this course?
This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:
stationarity and augmented Dicker-Fuller test
seasonality
white noise
random walk
autoregression
moving average
ACF and PACF,
Model selection with AIC (Akaike's Information Criterion)
Then, we move on and apply more complex statistical models for time series forecasting:
ARIMA (Autoregressive Integrated Moving Average model)
SARIMA (Seasonal Autoregressive Integrated Moving Average model)
SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)
We also cover multiple time series forecasting with:
VAR (Vector Autoregression)
VARMA (Vector Autoregressive Moving Average model)
VARMAX (Vector Autoregressive Moving Average model with exogenous variable)
Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:
Simple linear model (1 layer neural network)
DNN (Deep Neural Network)
CNN (Convolutional Neural Network)
LSTM (Long Short-Term Memory)
CNN + LSTM models
ResNet (Residual Networks)
Autoregressive LSTM
Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.
Content
Introduction
Introduction
What are Time Series?
Statistical Learning Approach: The Building Blocks
Basic Statistics
Setup for coding exercises
Coding Exercise: Descriptive and Inferential Statistics
Autocorrelation and White Noise
Stationarity and Differencing
Statistical Learning Approach: Basic Models
Random Walk
Coding Excercise: Random Walk
Moving Average Model
Coding Exercise: Moving Average Model
Autoregressive Model
Mini Project: Autoregressive Model
ARMA: Autoregressive Moving Average Model'
Coding Exercise: ARMA
Statistical Learning Approach: Advanced Models
ARIMA: Autoregressive Integrated Moving Average Model
Project 1: ARIMA
SARIMA
Project 2: SARIMA
AIC: Akaike's Information Criterion
SARIMAX
Project 3: SARIMAX
General Modelling Procedure
VAR: Vector Autoregressions
Project 4 - Part 1: VAR
Project 4 - Part 2: VARMA
Project 4 - Part 3: VARMAX
Deep Learning Approach: Theory
Introduction
Deep Neural Networks (DNN)
Recurrent Neural Network and Long Short-Term Memory (RNN and LSTM)
Convolutional Neural Networks (CNN)
Deep Learning Approach: End-to-end Project
Project 5 - Part 1: Initial setup
Project 5 - Part 2: Exploratory Data Analysis (EDA)
Project 5 - Part 3: Feature Engineering
Project 5 - Part 4: Data Windowing and Training Function
Project 5 - Part 5: Single Step Models
Project 5 - Part 6: Multi Output Models
Project 5 - Part 7: Multi Step Models
Conclusion and References
Congratulations and Thank You!
References
Screenshots
Reviews
Cwozniak
September 28, 2023
It's been great! I wish some of the functions and steps that we are doing would be explained more but it's fine so far.
Juan
September 26, 2023
Good, I expected to code a lot and that's what I've been doing. However, a lot of statistical knowledge is assumed and not explained thoroughly.
Patrick
September 14, 2023
Very good course full of real applications and models. I would liked if it had more extense introduction explaining each model and tool
Sumit
August 11, 2023
I learned a lot and Marco is an amazing tutor. But frankly, I was surprised to see the amount of coding involved in this rather short course. More explanation of each step should help us follow the background logic well. Thanks.
Konstantinos
July 6, 2023
If someone has pure mathematical background on this , it is excellent videos series to go from theoretical to applied.
Hossein
May 26, 2023
A perfect course for people who already have good theoretical knowledge of time series and deep learning. The theoretical slides are too short and are not enough for beginners. The coding style of the instructor is great.
Aranya
May 17, 2023
Things were going decently well until Deep Learning Project came in. The guy did not explain anything about the Window Generator class he built. It seemed like he copied from some other code and just typed it during recording. After that the different types of model fitting was still ok. But unless we understand how the WindowGenerator is working, it becomes very difficult to understand the rest of it.
Also a bit of focus on the installation process and error handling would have been better in the Prophet project.
Suraj
May 2, 2023
First of all, thank you Marco for creating this useful content. Overall the course was a good introduction into time series forecasting but I believe a lot of issues still exists.
1. Most of the theory was skipped which I feel is important to even implement any algorithm effectively
2. I personally felt that some of the code was not correct, especially when Marco tried to get the forecasts
3. Deep learning module is extremely high level. Even though, I have some prior experience using DL for time series, it was hard for me to keep up.
4. Lastly. I think this course was posted a while back and hence the Q/A forum is not active. Therefore, you can find it difficult to get answers to your queries.
Andrea
January 5, 2023
Very little theory, if you had not done a course on time series in your past, you'd be lost. Also, very repetitive content in the coding of the various chapters. Not sure looking at someone coding the same thing over and over again is a good idea.
Paulo
December 1, 2022
I like the topics, but in the section 6 that covers deep learning, I would like to suggest replacing with more updated examples because the models used all them have worse performance that the baseline.
Gerardo
November 5, 2022
Overall good but a lot of times the code was too complicated for no good reason. Simplify the code and focus more on explanation and understanding.
Andrés
September 9, 2022
The exercises are very useful but I would have preferred to have more matematical explanations instead of seeing the coding process
Mark
August 23, 2022
Would like a little more in depth explanation on each model type but overall great information and not too difficult to follow along
Aishah
August 18, 2022
I prefer would be better to go through different model with the same dataset and exercise challenge using different dataset, to have a thorough understanding.
However, It is a very good course, clearly explained. Good job Marco!
TJobson
July 28, 2022
Seems decent, 25% in.. but not the most visual and stimulating style. Would've been MUCH more interesting if it used Stock market examples. Biggest gripe was said entry level, basic skills needed, then there was NO SETUP section, a paragraph that said to just "clone the github repo" and off you go !?? No instructions nor recommendations on installing jupyter, what versions of anaconda, what rev of Python is used or needed, what OS is better to use, etc.. ??? Just a dry intro on statistics that pretty much anyone in CompSci took as a 101, and most looking for more of the Python Jumpstart at DataSci up-front chapter or 2 lead-in. A built-up Q&A section that had this detail should be there also as a backup, but Nada.. just some specific Q's from students. More details on python, libraries used, how they compare, why, etc.. Very basic, just types in or imports some code without much explanation of the bigger picture.
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3667582
udemy ID
11/29/2020
course created date
1/9/2021
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