A case study is an in-depth, detailed study of an individual or small group of individuals. These studies are generally qualitative in nature, and they consequently provide descriptive explanations of behavior or experiences. Quantitative research usually asks who, what, where, how, and how many questions. There are several types of case study methods, such as examples, exploration, stacking, and critical instances. Some advantages are that case studies can be more flexible than many other types of research, allow researchers to discover and explore as their research progresses, highlight in-depth content, and more. There are few downsides that the uniqueness of the data usually implies. Reproduction is impossible; case studies have some degree of subjectivity; researcher bias can be a problem; and there are concerns about the reliability, validity, and generalization of the results. Prediction is a method or technique of estimating future aspects. Forecasting is important for short- and long-term decisions. Companies can use forecasting in several areas, including technical forecasting, economic forecasting, and demand forecasting. The time series method makes predictions based only on historical patterns in the data. In a time series, measurements are taken over a continuous point or over a continuous period. The smoothing method (stable series) is suitable when the time series does not show any significant effects of trend, periodic, or seasonal components. The moving-average method is the most widely used smoothing technique. Machine learning methods can be used for classification and prediction of time series problems. Data mining is an interdisciplinary sub-discipline of computer science and statistics with a holistic goal of extracting information (using intelligent methods) from a dataset and transforming the information into an understandable structure for further use. It refers to sampling a portion of a large population dataset that is too small (or may be too small) to do but these methods can be used to create new hypotheses to test against a larger data population.