Data smoothing is a statistical technique used to reduce noise and fluctuations in data sets, making it easier to identify trends and patterns. This process involves applying algorithms or methods to create a smoother version of the original data, which can help in better decision-making and analysis. Common methods of data smoothing include moving averages, exponential smoothing, and kernel smoothing. These techniques are widely used in various fields, such as finance, economics, and environmental science, to enhance the clarity of data visualizations and improve forecasting accuracy.