Exponential Smoothing for Time Series Forecasting in Data Science

The statistical method most frequently used to predict a time series is called Exponential Smoothing. Utilizing the exponential window function, we employ this simple yet effective forecasting technique to smooth univariate time series data.
How Does Exponential Smoothing Work?
An approach to forecasting univariate time series data uses exponential Smoothing. According to the theory behind time series approaches, a prediction is a weighted linear sum of previous observations or lags. The exponential smoothing time series approach operates by giving historical observations weights that are exponentially diminishing. It is so named because the weight given to each demand observation decreases exponentially.

The model makes the assumption that the near future will resemble the recent past in some ways. Exponential Smoothing only picks up on one pattern from demand history: its level, or the average value, around which demand varies over time.

On the basis of user-made previous assumptions, such as seasonality or systematic tendencies, exponential Smoothing is typically used to anticipate time-series data.