the model' forecasts are bit off the mark (see figure - )but the exercise clearly demonstrates the possibilities here the forecast dataframe provides even more details about seasonalityweekly trendsand so on you are encouraged to explore this further prophet is based upon stan stan is statistical modeling language/framework that provides algorithms exposed through interfaces for all major languagesincluding python you may explore more on this at ummary this introduced the concepts of time series forecasting and analysis using stock and commodity price information through this we covered the basic components of time series along with common techniques for preprocessing such data we then worked on the gold price prediction use case this use case utilized the quandl library to get daily gold price information we then discussed traditional time series analysis techniques and introduced key concepts related to box-jenkin' methodology and arima in particular we also discussed techniques for identification and transformation of non-stationary time series into one using ad fuller testsacf and pacf plots we modeled the gold price information using arima based on statsmodel apis while developing some key utility functions like auto_arima(and arima_gridsearch_cv(key insights and caveats were also discussed the next section of the introduced the stock price prediction use case herewe utilized pandas_datareader to get & daily closing price information to solve this use casewe utilized rnn based models primarily we provided two alternative perspectives of formulating the forecasting problemboth using lstms the first formulation closely imitated the regression concepts discussed in earlier two-layer stacked lstm network was used to forecast stock price information the second perspective utilized timedistributed layer wrapper from keras to enable sequence modeling of the stock price information various utilities and key concepts were discussed while working on the use case finallyan upcoming tool (still in beta)prophet from facebook was discussed the tool is made available by facebook' data science team to perform forecasting at scale we utilized the framework to quickly evaluate its performance on the same stock price information and shared the results multitude of techniques and concepts were introduced in this along with the intuition on how to formulate certain time series problems stay tuned for some more exciting use cases in the next