Variable and Fixed Interval Exponential Smoothing
arXiv:1502.03465v11 citations
AI Analysis
This work addresses a fundamental issue in time series analysis for applications requiring efficient memory usage, but it appears incremental as it builds on existing exponential smoothing methods.
The paper tackles the problem of computing running averages for time series with both constant and variable observation intervals, defining and describing practical properties of exponential smoothers for these cases.
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.