Applied Time Series Analysis in Python

Applied Time Series Analysis in Python | Udemy [Update 02/2021]
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Genre: eLearning

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

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.

Who this course is for:
Beginner data scientists looking to gain experience with time series
Deep learning beginners curious about times series
Professional data scientists who need to analyze time series
Data scientists looking to transition from R to Python