Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 56 Minute(s)
Language
English
Taught by
Marco Peixeiro
Rating
Course Overview
Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis
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.
Course Content
- 8 section(s)
- 43 lecture(s)
- Section 1 Introduction
- Section 2 Statistical Learning Approach: The Building Blocks
- Section 3 Statistical Learning Approach: Basic Models
- Section 4 Statistical Learning Approach: Advanced Models
- Section 5 Deep Learning Approach: Theory
- Section 6 Deep Learning Approach: End-to-end Project
- Section 7 Conclusion and References
- Section 8 Bonus: Automated Time Series Analysis with Prophet
What You’ll 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
Skills covered in this course
Reviews
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HHermanus Combrink
The very first course's source code fails with multiple type errors and unknown libraries. No indication is given of what needs to be installed. I could not complete a code-along of chapter 1. Simple commands, like data.mean() does not work. I did not pay for a course to debug someone else's code. Avoid. I'll be asking for my money back.
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BBakr Abbas
I enjoyed how simple the structure was regarding each topic and it's implementation. Having the repetitiveness of applying the different models but with similar code is good in terms of practice and rigour, but that time could have been used to dive into more detail on theory. Even though a lot of knowledge is assumed, skimming over deep learning is not helpful when deciding what type of framework to use by intuition. In terms of code improvement, maybe making the classes that you wrote to be a bit more abstract could help them become more useable for different projects.
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AAranya Kundu
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.
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JJose Suarez
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.
