Description
Applied Time Series Analysis in Python course. This is the only course that combines the latest statistical techniques and deep learning for time series analysis. First, this course covers basic time series concepts:
- Stationarity and extended Dicker-Fuller test
- be seasonal
- White noise
- aimless walk
- Autoregression
- Moving average
- ACF and PACF,
- Model selection with AIC (Akaike information criterion)
We then apply more sophisticated statistical models to time series forecasting:
- ARIMA (autoregressive integrated moving average model)
- SARIMA (Seasonal autoregressive integrated moving average model)
- SARIMAX (integrated moving average model of seasonal regression with exogenous variables)
We also cover several time series forecasts with:
- VAR (vector autoregression)
- VARMA (vector autoregressive moving average model)
- VARMAX (vector autoregressive moving average model with exogenous variables)
Next, we move on to the Deep Learning section, where we use Tensorflow to apply various deep learning techniques to time 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)
- LSTM autoregression
What you will learn in the Applied Time Series Analysis in Python course
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Descriptive statistics versus inferential statistics
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Use deep learning to analyze time series with TensorFlow
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Linear models, DNN, LSTM, CNN, ResNet
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Automate time series analysis with Prophet
This course is suitable for people who
- Budding data scientists who want to gain experience with time series
- Deep learning beginners who are curious about time series
- Professional data scientists who need time series analysis
- Data scientists want to migrate from R to Python
Specifications of Applied Time Series Analysis in Python Course
- Editor: Udemy
- Teacher: Marco Peixeiro
- Training level: beginner to advanced
- Training duration: 6 hours and 5 minutes
- Number of courses: 40
Course topics on 7/2022
Prerequisites for the course “Applied Time Series Analysis in Python”.
- Basic knowledge of Python
- Basic knowledge of deep learning
- Jupyter notebook installed (or access to Google Colab)
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
English subtitles
Quality: 720p
Download link
free download software
Size
1.5GB