./nugetz

#time-series

27 packages tagged with “time-series

Dew.Stats.Core

Dew.Stats.Core is the cross-platform and managed-only edition of the Dew.Stats statistical computing library. It provides an extensive suite of tools for probability distributions, hypothesis testing, regression, multivariate analysis, time-series modeling, and experimental design. Built on top of Dew.Math.Core, it operates without native binary dependencies, making it suitable for Windows, Linux, macOS, mobile environments, plugins, sandboxed systems, and WebAssembly deployment. Probability Distributions and Random Models: - PDF, CDF, and inverse CDF for 36+ standard continuous and discrete distributions - Random number generators with parameter estimation and sampling workflows Descriptive and Exploratory Statistics: - Moments, quantiles, percentiles, ranks, scaling, normalization, cumulative transforms - Histograms, ogives, and distribution diagnostics Hypothesis Testing and Statistical Inference: - Parametric tests: t-test, Z-test, F-test, Chi-Squared, Bartlett, Hotelling T² - Non-parametric tests: Wilcoxon, Sign, Mann-Whitney, Anderson–Darling, Shapiro–Wilk, KS - Confidence intervals, residual analysis, goodness-of-fit evaluation Regression and Model Fitting: - Linear and multiple linear regression (weighted / unweighted) - Logistic, Poisson, Ridge regression, nonlinear regression (BFGS / CG / Marquardt / Simplex) - ANOVA and ANCOVA, principal component regression Multivariate and Structure Analysis: - PCA (covariance/correlation), PCA residuals, item analysis, rotational transforms - Classical Multidimensional Scaling and dimensionality significance tests Time Series Analysis and Forecasting: - ACF and PACF, moving-average and smoothing methods - ARMA/ARIMA/ARAR models with coefficient estimation via Yule–Walker, Burg, Innovations, and MLE - Box-Ljung significance tests and forecasting diagnostics Ready-to-Use High-Level Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling Integration Notes: - Uses Dew.Math.Core (managed vector/matrix computation, no native runtime dependency) - No visualization layer is included in the Core edition Dew.Stats.Core is ideal for portable analytics workflows, scripting, services, and embedded scientific computing.

v6.3.109.9K
dotnetcsharpcross-platformstatisticsregression

Dew.Stats.Linux

Dew.Stats.Linux is the Linux-native accelerated edition of the Dew.Stats statistical computing library. It provides a comprehensive suite of tools for probability distributions, hypothesis testing, regression, multivariate analysis, experimental design, and time-series modeling, powered by the high-performance vectorized numerical backend in Dew.Math.Linux. This edition is designed for Linux-based compute servers, HPC pipelines, analytics microservices, research clusters, data acquisition systems, and real-time industrial environments. Statistical Capabilities: - Probability distributions (PDF, CDF, inverse CDF) for 36+ discrete and continuous models - Random number generators and parameter estimation - Descriptive statistics, histograms, ogives, quantiles, outlier analysis Hypothesis Testing and Inference: - Parametric tests (t, Z, F, Chi-Squared, Bartlett, Hotelling T²) - Non-parametric tests (Wilcoxon, Sign, Mann–Whitney, Anderson–Darling, Shapiro–Wilk, KS) - Confidence intervals, residual diagnostics, model fitness evaluation Regression and Statistical Modeling: - Linear, multiple linear, logistic, Poisson, ridge and nonlinear regression - ANOVA and ANCOVA - Principal Component Regression and regularization workflows Multivariate and Structural Analysis: - PCA (correlation/covariance) with eigen decomposition - PCA residuals, factor rotation, Bartlett tests, item analysis - Classical Multidimensional Scaling and dimensionality interpretation Time Series Modeling and Forecasting: - ACF and PACF analysis - ARMA, ARIMA and ARAR models - Exponential smoothing (single/double/triple) - Box-Ljung significance testing and forecasting evaluation High-Level Statistical Workflow Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling Platform Characteristics: - Uses **Dew.Math.Linux** for native BLAS/LAPACK acceleration with AVX2/AVX512 dispatch - Highly scalable under multi-threaded workloads - No Windows or WinForms dependencies - Headless execution suitable for batch, service, and compute-node environments Dew.Stats.Linux provides the full analytical capabilities of Dew.Stats, optimized specifically for Linux-based CPU compute environments.

v6.3.108.1K
dotnetlinuxstatisticsregressionanova

Dew.Signal

Dew.Signal is a high-performance digital signal processing library built on top of Dew.Math, providing a comprehensive suite of optimized algorithms for real-time signal analysis, filtering, spectral estimation, modeling, and streaming signal workflows. The library is designed for scientific, engineering, audio, RF, vibration, instrumentation, control, and monitoring applications requiring both numerical accuracy and hardware-level performance on multi-core CPUs with AVX2/AVX512 support. Filter Design and Processing: - IIR filter design (Butterworth, Chebyshev I/II, Elliptic, Bessel), analog and digital domains - Order estimation, frequency transformations, bilinear and matched-Z transforms - State-space and zero-pole-numerator-domain modeling with group delay and stability analysis - FIR filter design using window methods and Remez exchange algorithm - FIR/Hilbert/differentiator/integrator design, Savitzky–Golay filtering, envelope detectors - Multi-rate filtering: half-band polyphase filters, decimation, interpolation, zoom-spectrum analysis Nonlinear and Adaptive Filters: - Sample-and-hold, sample-and-decay, and median filtering - High-quality rate conversion with 160dB stopband attenuation at high speed Spectral and Frequency-Domain Analysis: - FFT-based spectrum analyzer components with real-time UI integration support - Parametric spectral estimation: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, bispectrum, bicoherence, transfer function, coherence estimation - Peak interpolation and peak-tracking enhancements, phase unwrapping - Real and complex cepstrum, inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR measurements Signal Synthesis, Streaming, and Measurement: - Signal generators with stack-based vectorized function evaluation - Audio capture/playback with monitoring and triggering support - Data streaming and file format components for continuous acquisition and logging Forecasting and Time-Series Modeling: - Spectral forecasting based on controlled peak selection, enabling clear component-based prediction Integration and Extensions: - Works seamlessly with Dew.Math numeric structures (vectors/matrices) - Optional high-performance charting available via a separate Dew.Signal.Tee package (Windows visualization) - Part of the Dew Lab Studio ecosystem for unified math, DSP, and statistical analysis workflows Designed for reproducibility, determinism, and stable numerical behavior in long-running or real-time environments.

v6.3.105.7K
dotnetcsharpdspsignal-processingfiltering