Comprehensive Feature Set

Professional-grade tools for time series analysis, visualization, and automation

Data Analysis

Industrial-Strength Statistical Computing

Descriptive & Exploratory Analysis
  • Comprehensive descriptive statistics (mean, median, mode, variance, skewness, kurtosis)
  • Quartiles, IQR, and percentile calculations
  • Rolling statistics with configurable windows
  • Correlation analysis (Pearson, Spearman, Kendall Tau)
  • Multi-series correlation matrices with p-values
  • Distribution analysis and goodness-of-fit tests
Time Series Decomposition
  • STL (Seasonal-Trend decomposition using Loess)
  • Classical additive/multiplicative decomposition
  • X11 and X13-ARIMA-SEATS methods
  • Wavelet decomposition (Daubechies, Haar, Morlet, Mexican Hat)
  • Empirical Mode Decomposition (EMD)
  • Variational Mode Decomposition (VMD)
Forecasting & Prediction
  • ARIMA/SARIMA with automatic order selection
  • Exponential smoothing (Simple, Double, Triple/Holt-Winters)
  • State-space models (ETS framework)
  • Machine learning forecasters (Random Forests, Gradient Boosting)
  • Neural Networks for time series prediction
  • AutoML for automated model selection
  • Ensemble forecasting with confidence intervals
Anomaly & Pattern Detection
  • Statistical outlier detection (Z-score, Modified Z-score, Grubbs)
  • Generalized ESD test for multiple outliers
  • Machine learning methods (Isolation Forest, Local Outlier Factor)
  • One-Class SVM for novelty detection
  • Change point detection (CUSUM, PELT, Bayesian methods)
  • Matrix Profile for motif discovery
  • Seasonal Hybrid ESD for seasonal anomalies
Advanced Transformations
  • Data standardization (Z-score, Min-Max, Robust scaling)
  • Box-Cox and Yeo-Johnson transformations
  • Differencing and integration operations
  • Moving averages (Simple, Exponential, Weighted)
  • Kalman and Particle filtering
  • Savitzky-Golay smoothing
Spectral & Frequency Analysis
  • Fast Fourier Transform (FFT) and spectrograms
  • Power Spectral Density (Welch, Periodogram, Multitaper)
  • Wavelet transforms for time-frequency analysis
  • MUSIC and ESPRIT algorithms for frequency estimation
  • Cross-spectral analysis
Clustering & Similarity
  • Time series clustering (K-Means, K-Shape, Hierarchical)
  • Density-based clustering (DBSCAN, OPTICS)
  • Dynamic Time Warping (DTW) and FastDTW
  • Shape-based distance measures
  • Euclidean, Manhattan, Cosine similarity
  • Mutual Information and Transfer Entropy
Statistical Tests
  • Hypothesis testing (t-tests, ANOVA, Mann-Whitney U)
  • Non-parametric tests (Wilcoxon, Kruskal-Wallis)
  • Stationarity tests (ADF, Phillips-Perron, KPSS)
  • Cointegration tests (Johansen, Engle-Granger)
  • Chi-squared and Kolmogorov-Smirnov tests
  • Granger causality testing

Graphics & Visualization

Publication-Quality Charts with Interactive Exploration

Time Series Visualizations
  • Line plots with multiple series overlay
  • Scatter plots and bubble charts
  • Candlestick and OHLC charts for financial data
  • Volume and band charts
  • Area and step charts
  • Waterfall and Gantt charts
Statistical Plots
  • Histograms with kernel density estimation
  • Box plots and violin plots
  • Q-Q plots and P-P plots
  • Residual plots for model diagnostics
  • Autocorrelation (ACF) and Partial Autocorrelation (PACF)
  • Cross-correlation plots
Advanced Visualizations
  • Decomposition plots (trend, seasonal, residual)
  • Spectrograms and periodograms
  • Wavelet scalograms
  • Heat maps and correlation matrices
  • Contour and surface plots for 3D data
  • Phase plots and lag plots
Interactive & Export Features
  • Real-time zooming and panning
  • Crosshair and measurement tools
  • Data point inspection with tooltips
  • Multi-panel dashboards and facet grids

Export Formats:

  • Vector: SVG, PDF, EPS for publications
  • Raster: PNG, JPEG, TIFF with configurable DPI
  • Interactive HTML exports
  • LaTeX integration for academic papers

Programming & Scripting

Kusto Query Language (KQL) Integration & Multi-Language Support

KQL Example - Financial Market Analysis
// Analyze stock price volatility with rolling statistics
StockPrices
| where Symbol == "AAPL" and Timestamp between (datetime(2024-01-01) .. datetime(2024-12-31))
| extend
    Returns = (Close - prev(Close)) / prev(Close),
    MA_20 = moving_avg(Close, 20),
    MA_50 = moving_avg(Close, 50)
| extend
    Volatility = stdev(Returns) over (rows between 20 preceding and current row),
    BollingerUpper = MA_20 + 2 * Volatility,
    BollingerLower = MA_20 - 2 * Volatility
| extend Signal = case(
    Close > BollingerUpper, "Overbought",
    Close < BollingerLower, "Oversold",
    MA_20 > MA_50 and prev(MA_20) <= prev(MA_50), "Golden Cross",
    MA_20 < MA_50 and prev(MA_20) >= prev(MA_50), "Death Cross",
    "Hold"
  )
| project Timestamp, Close, MA_20, MA_50, Volatility, Signal
| render timechart with (title="AAPL Technical Analysis Dashboard")
Scripting Capabilities
  • C# scripting engine with Roslyn compiler
  • Python integration via IronPython
  • R interoperability for statistical packages
  • F# functional programming support
  • SQL-like queries with LINQ
  • Custom transformation pipelines
API & Extensibility
  • RESTful API for remote analysis
  • Plugin architecture (MEF-based)
  • Custom analysis plugins
  • Data source connectors
  • Export format extensions
  • Custom visualization types

Integration & Extensibility

Data Sources
  • CSV, Excel, Parquet, HDF5
  • SQL databases (PostgreSQL, SQL Server, MySQL, SQLite)
  • Time-series databases (InfluxDB, TimescaleDB, ClickHouse)
  • Cloud storage (Azure Blob, AWS S3, Google Cloud)
  • REST APIs and webhooks
  • MQTT and Kafka for streaming data
Export & Reporting
  • Interactive HTML dashboards
  • PDF reports with LaTeX quality
  • Excel workbooks with embedded charts
  • PowerBI and Tableau connectors (upcoming)
  • Automated email reporting
  • Web API for headless analysis
Enterprise Features
  • Role-based access control (upcoming)
  • Project versioning and collaboration
  • Scheduled analysis pipelines
  • Distributed computing for large datasets
  • GPU acceleration for deep learning
  • Audit logging for compliance

Ready to Transform Your Data Analysis?

Join the waitlist to be notified when TimeSeriesAnalyzer launches

Get Notified