Exponential smoothing for time series forecasting

Understanding Exponential Smoothing

Exponential smoothing is a method that assigns exponentially decreasing weights to past observations, giving more weight to recent data. It's ideal for smoothing out noise and uncovering underlying patterns.

Components of Exponential Smoothing

Exponential smoothing incorporates three key components: Level Trend Seasonality

Types of Exponential Smoothing

There are different variants of exponential smoothing like : Simple Double Triple Exponential Smoothing

Advantages of Exponential Smoothing

·   Simplicity ·   Adaptability to different data patterns ·   The ability to handle missing values and outliers effectively

Applications and Conclusion

– Exponential smoothing finds applications across industries, from finance to supply chain management. – Its versatility and effectiveness make it a valuable tool for forecasting.