forecasting stock and commodity prices in the so farwe covered variety of concepts and solved diverse real-world problems in this we will dive into forecast/prediction use cases predictive analytics or modeling involves concepts from data miningadvanced statisticsmachine learningand so on to model historical data to forecast future events predictive modeling has use cases across domains such as financial serviceshealthcaretelecommunicationsetc there are number of techniques developed over the years to understand temporal data and model patterns to score future events and behaviors time series analysis forms the descriptive aspect of such data and the understanding helps in modeling and forecasting the same traditional approaches like regression analysis (see and box-jenkins methodologies have been deeply studied and applied over the years more recentlyimprovements in computation and machine learning algorithms have seen machine learning techniques like neural networks or to be more specificdeep learningmaking headway in forecasting use cases with some amazing results this discusses forecasting using stock and commodity price datasets we will utilize traditional time series models as well as deep learning models like recurrent neural networks to forecast the prices through this we will cover the following topicsbrief overview of time series analysis forecasting commodity pricing using traditional approaches like arima forecasting stock prices with newer deep learning approaches like rnns and lstms the code samplesjupyter notebooksand sample datasets for this is available in the github repository for this book at under the directory/folder for time series data and analysis time series is sequence of observed values in time-ordered manner the observations are recorded at equally spaced time intervals time series data is available and utilized in the fields of statisticseconomicsfinanceweather modelingpattern recognitionand many more time series analysis is the study of underlying structure and forces that produced observations it provides descriptive frameworks to analyze characteristics of data and other meaningful statistics it also provides techniques to then utilize this to fit model to forecastmonitorand control there is another school of thought that separates the descriptive and modeling components of time series heretime series analysis typically is concerned with only the descriptive analysis of time series data to understand various components and underlying structure the modeling aspect that utilizes time series (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python