Making decisions in a variety of disciplines requires the use of forecasting, which makes predictions about future trends using data from the past and present. The basis is time series data that reveals patterns and trends, such as stock prices or sales information. Prior to using forecasting techniques, exploratory data analysis, or EDA, is essential for identifying underlying structures through analytical and visual inspection.

The type of data determines the method to use: machine learning (e.g., LSTM) for complex dependencies, ARIMA for time-dependent data, and linear regression for consistent trends. Preprocessing the data to remove outliers and missing values is essential. In order to enable model learning and evaluation on unobserved data, the train-test split is crucial. Measures of gauge accuracy include RMSE and MAE. Through this iterative process, forecasting techniques are improved and made more adaptable to shifting data patterns, enabling meaningful and well-informed decision-making.