Examine the concepts of time series with traditional to leading edge techniques using full-fledged examples of neural network models, such as artificial neural networks, convolutional neural networks, recurrent neural networks, and long short-term memory.The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing and ways of crafting the features. Next, you'll look at the traditional time series techniques like ARMA, ARIMA, SARIMA, VAR, VARMA with trending framework like stat models, pyramid, PyFlux. You'll then move on to building classification models using sktime, and see how to leverage advance deep learning-based techniques like RNN, LSTM, CNN, and Time Series Data Generator. The book also explains the popular framework fbprophet for modeling time series analysis.  provides you with a solid foundation to effectively work with different techniques of time series methods. All the code is available in Jupyter notebooks and python scripts, which enhances the hands-on experience. Data scientists, data analysts, and financial analysts  weiterlesen