Time Series Analysis

Time series problems are routinely encountered by Data Scientists. Whether it is in the form of a demand forecasting problem or anomaly detection problem, there are many applications of time series modelling in many industries. Though traditionally being a field of econometrics and dynamical systems, the Machine Learning community is catching up with time series modelling problems with new algorithms such as Recurrent Neural Networks, LSTMs and Transformer Networks. Time series modelling poses extra challenges for Data Scientists such as nonstationarity, serial correlation, heteroscedasticity, structural breaks, model validation and model stability. The practicing Data Scientist in the field will definitely encounter many problems involving time series forecasting. Therefore they should be equipped with theoretical knowledge and practice of time series modelling.