Smoothing techniques are methods used in data analysis to reduce noise and fluctuations in datasets, making trends easier to identify. These techniques help in creating a clearer picture of underlying patterns by averaging out short-term variations. Common smoothing methods include moving averages and exponential smoothing. These techniques are widely applied in various fields, such as finance, economics, and signal processing. By using smoothing, analysts can make more informed decisions based on the underlying trends rather than being misled by random variations. This is particularly useful in time series analysis, where data points are collected over time.