The end-to-end solution for building and hosting better data apps, dashboards, and reports.
What’s different? Analyzing Time-Series Forecast Performance
rawdata = FileAttachment("TexasEnergyDemandForecast@1.csv").csv({typed: true})
data = aq.from(rawdata) // https://observablehq.com/@observablehq/data-wrangler
.rename({demand: "actuals"}) // rename column from “demand” to “actuals”
.objects() // return an array of objects
event = ({
description: "Rolling Blackouts",
datetime: new Date("2021-02-15T07:25:00Z") // 7:25AM UTC is 1:25AM CST
})
zoom = {
const start = d3.isoParse("2021-02-07T07:25:00Z");
const end = d3.isoParse("2021-02-16T07:25:00Z");
return (d) => +d.date >= +start && +d.date <= +end;
}
zoomed = data.filter(zoom)
zoomedTicks = d3.utcDays(...d3.extent(zoomed, d => d.date)).map(d => d3.utcHour.offset(d, 6))
dataTicks = d3.utcWeeks(...d3.extent(data, d => d.date)).map(d => d3.utcHour.offset(d, 6))
colors = new Map([
["actuals", "#5b6187"],
["forecast", "#a2a5ba"],
["below", "#89119c"], // actuals below forecast
["above", "#ff6300"], // actuals above forecast
])
resp = {return width>500 ? 1: 2 }
demandBelowForecastAnnotation =
[
Plot.text([{value: "demand falls below forecast ➣"}], {
text: "value",
x: d3.utcHour.offset(event.datetime, -36),
y: -4000,
textAnchor: "end",
fontSize: resp*width/70,//16,
fontStyle: 'italic'
})
]
demandAboveForecastAnnotation =
[
Plot.text([{value: "demand rises above forecast ➣"}], {
text: "value",
x: d3.utcHour.offset(event.datetime, -134),
y: 6200,
textAnchor: "end",
fontSize: resp*width/70,//16,
fontStyle: 'italic'
})
]
// point out where the demand dropped below the forecast
didYouNoticeThis =
[
Plot.text([{value: "Did you notice demand drop below forecast here? ⇘"}], {
text: "value",
x: d3.utcHour.offset(event.datetime, -36),
y: 67000,
textAnchor: "end",
fontSize: 16,
fontStyle: 'italic'
})
]
// show when the rolling blackouts started on the difference chart
annotationsDeltas =
[
Plot.ruleX([event.datetime], {
stroke: "gray",
y1: 0,
y2: maxDelta
}),
// text describing the event of interest
Plot.text([event], {
text: "description",
x: "datetime",
y: maxDelta * 1.05,
textAnchor: "start"
}),
]
maxDelta = d3.max(data, d => d.actuals - d.forecast)
maxValue = Math.max(d3.max(data, d => d.actuals), d3.max(data, d => d.forecast))
import {textcolor} from "@observablehq/text-color-annotations-in-markdown"
import {authorship, navigation, workshop, altauthorship} from "@observablehq/timeseries-assets"
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.