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logistic-growth

Fit logistic growth models to time-series data to estimate r and K.

Updated Jan 26, 2026
Search Tags:#time-series#modeling#logistic-growth#population-dynamics#parameter-estimation#data-fitting
One-click install
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill logistic-growth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: logistic-growth
Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/logistic-growth
Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill logistic-growth

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured approach to fitting a logistic growth model to time-series data, enabling estimation of the intrinsic growth rate r and carrying capacity K, which are essential for understanding bounded growth in populations, epidemiology, and market dynamics.

Core Features & Use Cases

  • Analytical & numerical fitting: supports nonlinear least squares and linearized methods to estimate N0, r, and K from observed data.
  • Interpretability: exposes analytical solutions, the inflection point, and stability insights (N=0 unstable, N=K stable) for clear interpretation.
  • Visualization & forecasting: offers plotting utilities to compare observed data with model predictions and to visualize growth dynamics over time.

Quick Start

  • Prepare your time points t (e.g., days) and observed population N(t).
  • Run nonlinear fitting to estimate N0, r, and K with fit_nonlinear(t, N) or use fit_linearized(t, N) for a quick check.
  • Inspect the fitted parameters (N0, r, K) and goodness-of-fit, then visualize the results with plot_logistic_fit.

Frequently Asked Questions about logistic-growth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fit a logistic growth model to time-series data?▼

Estimate intrinsic growth rate and carrying capacity by applying nonlinear least squares or linearized fitting methods to your time-series data, yielding initial population, rate, and saturation parameters for bounded growth analysis.

What is the difference between nonlinear and linearized fitting for logistic growth parameter estimation?▼

Nonlinear least squares fitting directly estimates logistic growth parameters from observed time-series data, whereas linearized fitting provides a quick analytical approximation of growth rate and carrying capacity values.

Can I use logistic growth modeling for epidemiology and technology diffusion data?▼

Logistic growth modeling applies to epidemiology, population dynamics, and technology diffusion by estimating intrinsic growth rate and carrying capacity to understand and forecast where observed growth saturates over time.

How do I visualize and forecast population dynamics using a fitted logistic model?▼

Use the provided plotting utilities to compare observed time-series data with model predictions, visualizing the inflection point and growth dynamics over time to forecast bounded population saturation.

When should I not use a logistic growth model for time-series data fitting?▼

Avoid logistic growth modeling if your time-series data does not exhibit bounded growth or saturation, because parameter estimation for growth rate and carrying capacity relies on the population approaching a stable equilibrium.

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