The Simple Moving Average (SMA) filter is a fundamental technique in signal processing and time series analysis. It is used to smooth out short-term fluctuations and highlight longer-term trends or cycles in data. This README provides an overview of SMA filters, their implementation, and considerations when using them.
The Simple Moving Average (SMA) is computed by taking the arithmetic mean of a set of values over a specified window of time or space. It is a linear filter that evenly weights all data points within the window.
- Simplicity: Easy to understand and implement.
- Noise Reduction: Effective in smoothing out noise and short-term fluctuations.
- Preservation of Trends: Maintains the overall trend of the data while reducing noise.
- Lag: SMA introduces a lag between the filtered output and the original data due to its averaging nature.
- Less Effective for Non-Stationary Data: Performs best on stationary data; less effective with data containing trends or seasonality.
- Window Size Selection: The choice of window size affects the trade-off between noise reduction and responsiveness to changes.