Some of them are:

• Moving Average Smoothing: This method involves taking the average of a subset of data points to smooth out short-term fluctuations.
• Exponential Smoothing: This method is similar to moving average smoothing but assigns exponentially decreasing weights to older observations.
• ARIMA: This method is used for time series data with trends and seasonality. It models the autocorrelation in the data using three parameters: p (autoregressive), d (differencing), and q (moving average).

There are several post-processing methods available for univariate time series analysis. One common approach for modeling univariate time series is the autoregressive (AR) model. Another approach is the linear time-series processes which is the standard tool of time-series analysis. Linear time series process can always be expressed as Y t = δ t+ Y 0 + Xt i=0 θ iϵ t−i Linear in the errors δ t is a purely deterministic process {ϵ t}is a White Noise process Example of non-linear processes GARCH(1,1) Y t ∼N(0,σ2 t) σ2 t = ω+ αY2 t−1 + βσ 2 t−1 12.

There are several post-processing methods available for univariate time series analysis. Some of the most common methods include:

• Moving average smoothing
• Exponential smoothing
• Autoregressive integrated moving average (ARIMA) modeling
• Seasonal decomposition of time series (STL)
• Wavelet analysis

In summary, some of the post-processing methods available for univariate time series analysis include moving average smoothing, exponential smoothing, ARIMA modeling, STL, and wavelet analysis.

Some of the post-processing methods available for univariate time series analysis include moving average smoothing, exponential smoothing, ARIMA modeling, STL, and wavelet analysis.

That depends on what is meant by post-processing, or more specifically what is being post-processed. One example is post-processing of physics-based models in order to forecast weather, or renewable energy. In general, physics models are more accurate at longer horizons or lower frequencies but are less accurate at short horizons or higher data resolutions. Given this, it has become customary to post-process the output from physics models, at least in predicting over short intervals. Any number of statistical techniques can be used. The most common is the model output statistics method, in which the physics model output is moved up and down in relation to recent data. This method actually goes back to the 1970s. See on this issue: H.R. Glahn, D.A. Lowry (1972). The use of model output statistics (MOS) in objective weather forecasting, Journal of Applied Meteorology, 11, 1203-1211.