Times Series Methods are the many approaches used to analyze data over a certain period.
The key is to pick the right forecasting approach, taking into account the specifics of the time-series data.
Data smoothing is a statistical method used in time series forecasting that eliminates anomalies to better see a trend. There will always be an element of chance in any set of data compiled over time. As a result of smoothing, cyclical, and trending patterns may be seen through the noise in the data.
The moving-average model (MA model), often referred to as the moving-average process, is a popular technique for modeling univariate time series in the field of time series prediction. The output variable is stated to be linearly dependent on the current and various past values of a random component within the context of the moving-average model.
The MA model, along with the autoregressive (AR) model, is a specific example and fundamental aspect of the more general ARMA and ARIMA time series models, which have a more intricate stochastic structure.
The finite MA model, in contrast to the AR model, remains inert at all times.
Exponential Smoothing Model
Using the exponential window function, exponential smoothing is a method for reducing noise in time series data. If you want to make a conclusion based on certain assumptions you already have about the data, then look into using exponential smoothing. Single, double, and triple are all subcategories of the broader category of it.
Modeling with Moving Averages vs. Exponential Smoothing
Unlike the ordinary moving average that uses a constant weighting of all prior data, exponential functions utilize ever-decreasing weights based on how far back in time the observations are.
Observation weights in moving averages are all equal to 1/N. However, with exponential smoothing, the weights assigned to the data depend on one or more smoothing parameters, which must be computed.