Trend Detector - Statistical Significance Analyzer

Pro v1.0.0 1 view

Detect statistically significant trends in time series data using Mann-Kendall tests, regression analysis, and change point detection. Replace gut-feel chart analysis with rigorous statistics.

What You Get

Replace guesswork with statistical certainty when analyzing trends in your business metrics and time series data.

The Problem

You need to know whether changes in your metrics are statistically significant or just random noise. Looking at charts might suggest growth or decline, but is it real? Did something actually change on that date, or is that just variation? Making data-driven decisions requires distinguishing true trends from noise with statistical confidence.

The Solution

This skill applies rigorous statistical methods to your time series data using Mann-Kendall tests for non-parametric trend detection and linear regression analysis. It quantifies growth rates with confidence intervals and identifies whether trends are statistically significant. The skill detects change points using CUSUM methodology to pinpoint specific dates when trends shifted significantly. If your data has recurring patterns like quarterly peaks or weekly cycles, seasonality analysis decomposes these patterns separately from underlying growth trends. All statistical findings are translated into plain English with confidence levels, visual indicators, and actionable recommendations.

How It Works

  1. 1 Validate CSV data quality, check for missing values, and confirm sufficient data points for analysis (minimum 12, preferably 24+)
  2. 2 Load time series data using pandas and prepare it for statistical analysis with proper date parsing
  3. 3 Apply Mann-Kendall test and linear regression to identify statistically significant trends with confidence intervals
  4. 4 Detect change points using CUSUM methodology to identify dates where trends shifted significantly
  5. 5 Analyze seasonality patterns to separate recurring cycles from underlying growth trends
  6. 6 Generate visualization showing time series, trend lines, confidence bands, and change points
  7. 7 Produce comprehensive report translating statistical findings into plain business language with recommendations

What You'll Need

  • Time series data in CSV format with date/timestamp and metric columns
  • Minimum 12 data points (preferably 24+ for robust analysis)
  • Python with scipy, statsmodels, numpy, pandas, and matplotlib packages

Get This Skill

Requires Pro subscription ($9/month)

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