`RollingMean`

Description

The RollingMean (also known as moving average) class computes the mean value within a moving window of specified size over a sequence of data.

Parameters:

window_size: Specifies the size of the rolling window.
start_policy: Defines how the function handles the initial phase when fewer than window_size data points are available. This parameter accepts one of the following three values:
- "strict": Returns NaN for all calculations until window_size elements have been processed.
- "expanding": Adapts the computation by dynamically reducing the window size to include all available data, starting from a single point and growing until window_size is reached.
- "zero": Simulates a full initial window of zeros, effectively pre-filling the data stream with window_size zeros before processing the actual input.

Usage Example and Plot

Below is an example of using RollingMean to calculate the rolling mean for a random dataset, along with a plot illustrating its output.

import numpy as np
import plotly.graph_objects as go
from screamer import RollingMean

# Generate example data
N = 300
window_size = 30
data = np.cumsum(np.random.normal(size=300))


# Plotting with Plotly
fig = go.Figure()
fig.add_trace(go.Scatter(y=data, mode='lines', name='Input Data'))
fig.add_trace(go.Scatter(y=RollingMean(10)(data), mode='lines', name='Rolling Mean 10', line=dict(color='red')))
fig.add_trace(go.Scatter(y=RollingMean(60)(data), mode='lines', name='Rolling Mean 60', line=dict(color='green')))
fig.update_layout(title=f"Rolling mean with Window Size 10 and 60",
    xaxis_title="Index",
    yaxis_title="Value",
    margin=dict(l=20, r=20, t=80, b=20),
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)
fig.show()

Implementation Details

Algorithm

RollingMean implements a cyclic buffer.

Complexity

Time Complexity: O(1)) per new element due to the insertion and deletion operations in the cyclic buffer.
Space Complexity: O(window_size), as only elements within the current window are stored.

Performance

Short streams (n=1.000): 450% faster than Pandas Rolling mean and 50% faster than a numpy cumsum based approach.
Longer streams (n=1.000.000): 450% faster than Pandas Rolling mean and 270% faster than a numpy cumsum based approach.

RollingMean