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Chris Campbell Gaussian Processes

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Chris Campbell

Gaussian Processes

Forecasting Processes

Agenda

Gaussian Processes

Profit

Code Speed

Forecasting Processes

Project Structure & Docs

Packaging & Testing

Reproducible Data

Reproducible Data

Raw Data

1 run_all.R

CommitScripts

f34d20 sales

Data Checksum

02ec42

2 run_all.R 932a45 sales 563b3e

3 run_all.R 932a45 sales 74a10d

Agile Planning & Tickets

Cross-Validation

Model Ensemble

Gaussian Processes

Joint Distribution

Conditional Probability

Time Series

Distribution Next Point

package:GPfit

library(GPfit)gp2 <- GP_fit(

X = xm1, Y = y1)

Points in Time

Kernel

𝐾(𝑥, 𝑥) =

𝑘(𝑥1, 𝑥1 ) 𝑘(𝑥1, 𝑥2 ) 𝑘(𝑥1, 𝑥𝑛 )𝑘(𝑥2, 𝑥1 ) 𝑘(𝑥2, 𝑥2 ) 𝑘(𝑥2, 𝑥𝑛 )𝑘(𝑥𝑛, 𝑥1 ) 𝑘(𝑥𝑛, 𝑥2 ) 𝑘(𝑥𝑛, 𝑥𝑛 )

Slow in High Dimensions

Code Speed

What is taking longest?

library(profvis)profvis(gp2 <- GP_fit(X = xm1, Y = y1))

Timing Hierarchy by Function

Flame Graph

Replace Slower Code

Profit

Forecasts aggregatedmonthly

GitHub & CRAN

https://builder.r-hub.io/

Uncertainty

Gaussian Processes

Derive non-linear relationships from observed data

Excellent for interpolation

Allow estimation of forecast error

Poor performance for higher dimensionality