Today, I learned about Time series, a sequence of data points arranged in chronological order, recorded at consistent, equally spaced intervals. Time series data is widely used in various disciplines, including environmental studies, biology, finance, and economics. The primary aim when working with time series is to understand the patterns, trends, and behaviors that emerge in the data over time. Time series analysis involves tasks such as modeling, interpreting, and predicting future values by leveraging historical data trends. In forecasting the lifecycle of a project, one anticipates future trends or outcomes based on past data. This lifecycle typically includes stages such as data collection, performing exploratory data analysis (EDA), model selection, training, validation and testing, deployment, and ongoing monitoring and maintenance. This systematic process is crucial for maintaining accurate and current forecasts, requiring periodic updates and refinements.
Baseline models serve as simple initial benchmarks or reference points against which more complex models can be compared. They provide a fundamental level of prediction, which is useful for evaluating the performance of more sophisticated modeling techniques.