SignalSharp 0.1.8

SignalSharp

SignalSharp is a .NET library focused on time series analysis, change point detection, and signal smoothing/filtering tasks. It provides implementations of common algorithms optimized for clarity and efficiency.

Core Features

Change Point Detection

Algorithms to identify points in time where the statistical properties of a signal change.

• PELT (Pruned Exact Linear Time):

  • Detects multiple change points efficiently.
  • Supports exact (Jump = 1) and approximate (Jump > 1) modes for speed/accuracy trade-offs.
  • Configurable via cost functions to target specific types of changes:
    • L1CostFunction: Robust to outliers, sensitive to median shifts.
    • L2CostFunction: Sensitive to mean shifts, assumes constant variance (computationally efficient).
    • GaussianLikelihoodCostFunction: Sensitive to changes in both mean and variance, assumes normality (computationally efficient, supports BIC/AIC).
    • PoissonLikelihoodCostFunction: For count data, sensitive to changes in event rate (supports BIC/AIC).
    • BernoulliLikelihoodCostFunction: For binary (0/1) data, sensitive to changes in success probability (supports BIC/AIC).
    • BinomialLikelihoodCostFunction: For success/trial data, sensitive to changes in success probability (supports BIC/AIC).
    • RBFCostFunction: Kernel-based, can detect complex changes in the underlying distribution shape.
    • ARCostFunction: Fits an Autoregressive model, sensitive to changes in signal dynamics/autocorrelation (univariate only, supports BIC/AIC).
  • Includes PELTPenaltySelector for automatic penalty selection using BIC, AIC, or AICc for supported likelihood-based cost functions.

• CUSUM (Cumulative Sum):

  • Detects shifts in the mean of a signal.
  • Works by accumulating deviations from an expected level and triggering when a threshold is exceeded. Useful for process monitoring.

Signal Smoothing

Methods to reduce noise from measurements.

• Savitzky-Golay Filter: Smooths data by fitting successive sub-sets of adjacent data points with a low-degree polynomial using linear least squares. Helps preserve signal features better than a simple moving average.
• Moving Average: Basic smoothing technique that calculates the average of data points within a sliding window.

Utilities

• Signal Padding: Methods for common padding modes (Constant, Mirror, Nearest, Periodic).
• Statistical Functions: Basic statistics (Mean, Variance, StdDev, Median, Normalization, etc.).

Installation

Install the package via the dotnet CLI:

dotnet add package SignalSharp

Or use the NuGet Package Manager in your IDE.

Usage

For detailed examples and API documentation, please refer to the official documentation.

Here's a quick example of how to use the PELT algorithm for change point detection:

using SignalSharp.Detection.PELT;
using SignalSharp.CostFunctions.Cost;

// Create a sample signal
double[] signal = { 1, 1, 1, 5, 5, 5, 1, 1, 1 };

// Initialize PELT algorithm (using default L2 cost, MinSize=1, Jump=1)
var options = new PELTOptions(); 
var algo = new PELTAlgorithm(options);

// Detect change points with a manually chosen penalty
int[] breakpoints = algo.FitAndDetect(signal, penalty: 2.0); 
Console.WriteLine($"Manual Penalty Change Points: {string.Join(", ", breakpoints)}"); // Expected: [3, 6]

// --- Or use Automatic Penalty Selection (requires a likelihood cost function) ---

// Configure PELT with Gaussian Likelihood (supports BIC/AIC)
var likelihoodOptions = new PELTOptions { CostFunction = new GaussianLikelihoodCostFunction(), MinSize = 2 };
var likelihoodAlgo = new PELTAlgorithm(likelihoodOptions);

// Create the selector
var selector = new PELTPenaltySelector(likelihoodAlgo);

// Choose selection method (e.g., BIC)
var selectionOptions = new PELTPenaltySelectionOptions(PELTPenaltySelectionMethod.BIC);

// Fit data and select penalty
var result = selector.FitAndSelect(signal, selectionOptions);

Console.WriteLine($"Auto Penalty (BIC): {result.SelectedPenalty:F4}");
Console.WriteLine($"Auto Penalty Change Points: {string.Join(", ", result.OptimalBreakpoints)}"); 
// Expected: likely [3, 6], penalty will vary

Contributing

Contributions are welcome! If you have ideas, suggestions, or bug reports, feel free to open an issue or submit a pull request.