The agenda of the Time Series workshop is to bring students together in the analysis of time series and methods to process sequential data. This includes varied algorithms for time series analysis, feature engineering for single and multi-time series, prediction/forecasting, clustering, anomaly and change point detection. Time series data can be found in many fields of our daily life, such as sales, stocks, traffic etc. We see time series in retail in the form of sales, inventory, labor allocation etc. and to forecast them accurately is crucial for success in retail business. In this workshop, Neelabh Pant will give an introduction on time series and its applications. We will begin by understanding the domain of the data and about autocorrelation and partial autocorrelation charts and using them in conjunction with powerful ARIMA based models, including seasonal-ARIMA models and SARIMAX to include Exogenous data points. We will also talk about state-of-the-art deep learning techniques with RNNs (Recurrent Neural Networks) that use deep learning to forecast future data points. Finally, we will cover Facebook’s Prophet library, a simple to use, yet powerful Python library developed to forecast the future.