Applied Time Series Analysis in Python

[LeeAndro]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 40 lectures (6h 5m) | Size: 1.48 GB

This is the only course that combines the latest statistical and deep learning techniques for series analysis.

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

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 series analysis with Tensorflow

Linear models, DNN, LSTM, CNN, ResNet

Basic knowledge of Python

Basic knowledge of deep learning

Jupyter notebook installed (or access to Google Colab)

First, the course covers the basic concepts of 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 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 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 s 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.

Bner data scientists looking to gain experience with series

Deep learning bners curious about s series

Professional data scientists who need to analyze series

Data scientists looking to transition from R to Python



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