Public
Edited
Mar 25, 2023
timeSeries = {
const runs = []
for (let r = 0; r < numberOfRuns; r = r+1) {
const run = []
run[0] = {time: timeSteps[0], value: initialValue }
const noiseSteps = Array.from({length: numberOfSamples}, d3.randomNormal(0, Math.sqrt(timeStepDuration)))
for(let i = 1;i < numberOfSamples+1;i = i + 1){
const previousValue = run[i-1]['value']
run[i] = {time: timeSteps[i], value: (muStep * previousValue) + (previousValue * sigma * noiseSteps[i-1]) }
}
runs.push(run)
}
return runs
}
growthRateStep = Math.pow(growthRate, timeStepDuration)
finalValues = timeSeries.map((run) => (run[numberOfSamples-1]['value']))
finalEnsembleAverage = finalValues.reduce((a, b) => (a + b))/numberOfRuns
finalExpectEnsembleAverage = initialValue * Math.pow(mu, timePeriod)
pachinko(noiseSteps2)
test= Math.sqrt(0.1)
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
