Marple AI

Automatically decomposes time series data into trend, seasonal, and residual components without requiring manual statistical configuration. Eliminates the need to write decomposition code from scratch.

Identifies statistical outliers and anomalies in time series data using built-in algorithms. Flags unusual patterns without requiring manual threshold setting or algorithm selection.

Generates future value predictions for time series data using automated model selection and training. Produces forecasts with confidence intervals without requiring users to choose or tune forecasting algorithms.

Provides a shared environment where multiple team members can view, annotate, and discuss time series analyses in real-time. Enables teams to collaborate without exporting data or switching between tools.

Generates interactive charts and graphs for time series data with built-in exploration tools like zooming, panning, and hover details. Allows users to explore data patterns visually without coding.

Automatically calculates and displays key statistical metrics for time series data including mean, variance, autocorrelation, and seasonality strength. Provides instant statistical context without manual calculation.

Handles loading time series data from various sources and performs basic preprocessing like handling missing values, resampling, and time index alignment. Prepares raw data for analysis without manual data cleaning code.

Automatically identifies and highlights significant patterns, trends, and insights in time series data. Surfaces key findings without requiring manual pattern analysis or statistical testing.

Provides a free tier with functional time series analysis capabilities for exploratory work and testing. Allows users to validate workflows and learn the platform before committing to paid plans.