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For First Responders · Time-Series Forecaster

Time-Series Forecaster for a conversion funnel

Built for first responders planning or training for real incidents. Run fast what-if scenarios for response planning and training — no software to install in the field. Simulate a conversion funnel live below — adjust the inputs and watch it respond, right in your browser.

1

Linear Regression

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Linear RegressionLive

Controls

Presets

Ordinary least squares finds the line that minimizes the sum of squared vertical residuals (the gray drop-lines). R² measures the fraction of variance the line explains — 1 is perfect, 0 is useless. Add noise or remove points to watch the fit and R² degrade.

▶ Run in Python

Data Inspector

Fitted slope0.000
Intercept0.000
0.000
Correlation r0.000

Governing equation

Reading this result: R² = 0.00: noise (2) swamps the signal, so least squares can barely tell the slope from flat — adding data points would tighten the estimate.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Fourier Series BuilderLive

Controls

Add harmonics and watch sine waves sum into a square, sawtooth, or triangle wave — the Fourier series in action.

Presets

▶ Run in Python

Data Inspector

Wavesquare
Harmonics8
Basissine

Governing equation

Reading this result: Reconstructing the square wave from 8 sine harmonics. More harmonics sharpen the reconstruction and pull it closer to the ideal shape. But at each discontinuity the Gibbs phenomenon leaves a persistent ~9% overshoot spike — adding terms only narrows that ripple, it never removes it, no matter how many harmonics you sum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

3

Kalman Filter

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Kalman FilterLive

Controls

Presets

The Kalman filter fuses a motion model with noisy measurements to track a hidden state optimally. Each step it predicts, then corrects using the Kalman gain — trusting the measurement more when the model is uncertain, and vice versa. It smooths the jittery sensor (gray) into a clean estimate (cyan) that hugs the truth. The math behind GPS, radar, and spacecraft navigation.

▶ Run in Python

Data Inspector

Measurement RMSE0.0
Kalman RMSE0.0
Noise reductionNaN%

Governing equation

Reading this result: Measurement noise dwarfs process noise, so the Kalman gain stays small — the filter leans on its motion model and smooths the jitter hard.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Monte Carlo Price SimulationLive

Controls

Presets

Geometric Brownian motion models a price with constant drift and random volatility — the assumption behind Black-Scholes. Running hundreds of simulated paths reveals the full distribution of outcomes, not just an average. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Median outcome$0
5th percentile$0
95th percentile$0

Governing equation

Reading this result: Median outcome is about $0, just under the drift-only $107 — GBM compounds in log-space, so volatility always pulls the typical path below the average.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for first responders?
Yes — this version of "Time-Series Forecaster for a conversion funnel" is framed for first responders planning or training for real incidents. Run fast what-if scenarios for response planning and training — no software to install in the field.
Do I need to install anything?
No. It runs in any modern browser, free, with no account required.