Applied Time Series Analysis in Python

Applied Time Series Analysis in Python

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.

Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis

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What you will learn

• Descriptive vs inferential statistics
• Random walk model
• Moving average model

Rating: 4.3169

Level: Intermediate Level

Duration: 7 hours

Instructor: Marco Peixeiro