Applied Time Series Analysis in Python | Data Science Online Course by Udemy

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

An excellent training about Data Science

Applied Time Series Analysis in Python

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 testseasonalitywhite noiserandom walkautoregressionmoving averageACF 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 modelsResNet (Residual Networks)Autoregressive LSTMThroughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.

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