Applied Time Series Analysis in Python

• 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 thatcombines 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 InformationCriterion)

Then, we move on and apply more complex statistical models for time series forecasting:

• ARIMA (Autoregressive Integrated MovingAverage model)

• SARIMA (Seasonal Autoregressive Integrated MovingAverage model)

• SARIMAX (Seasonal Autoregressive Integrated MovingAverage 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.