Exponential Moving Average calculates exponentially weighted moving average
This calculation analyses data values by applying weighting factors that decrease exponentially, to older data.
Defines the time series data fed into the function. This can be a sensor ID or another function.
Number of data intervals considered in the function
*Input data is optional in most cases. If Info360 detects that the first input is time series data, it will be applied to the function. Otherwise, the current active sensor's data will be used, which is often the case in Reference Charts.
Example Usage as an Expression:
EMA(Sum(4),10)): Find exponential moving average for data set when every 4 data points are added. Evaluates for the last 40 intervals.
Examples Reference Chart:
The following example shows a comparison of pump flowrate data and multiple moving average methods with the same period.
The TMA method displays the smoothest line with the most delay from the original signal, while WMA most closely follows current data.
Regression responds the fastest to sudden changes in measured values, but this also makes it the most sensitive to spikes in data.
The Median function preserves the on/off gradient in the data the best, but it will be offset by half of the selected period.
Also note that EMA and TMA provide dampened smooth values for a longer window than the input period.
For information on setting up custom equations and syntax, please refer to Analytical Functions.