Today I learned about time series. A time series refers to a chronological sequence of data points consisting of measurements or observations made at uniform and regular intervals. This data format is widely used in various fields such as environmental sciences, biology, finance and economics. When dealing with time series, the main goal is to understand the inherent patterns, trends, and behaviors that may appear in the data over time. Time series analysis includes e.g. model, interpret and predict future values ​​based on historical trends. Project life cycle forecasting involves predicting future trends or outcomes based on historical data. The life cycle typically includes steps such as data collection, exploratory data analysis (EDA), model selection, model training, validation and testing, deployment, monitoring, and maintenance. This cyclical approach ensures accurate and up-to-date forecasts that require regular checking and correction. Base models are simple reference points or reference points for more complex models. They provide a basic forecast that helps evaluate the performance of more advanced models.