0.5.4 • Published 9 months ago
@bsull/augurs v0.5.4
augurs: time series forecasting
Javascript bindings to the augurs
time series framework.
Usage
- Add the package to your dependencies:
"dependencies": {
"@bsull/augurs": "^0.4.1"
}
- Import the default function and initialize once somewhere in your application:
import init from "@bsull/augurs";
init().then(() => console.log("Initialized augurs"));
- Use the various ETS, changepoint, outlier, or seasonality detection algorithms. For example:
import { ets, seasonalities } from "@bsull/augurs"
const y = new Float64Array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0]); // your time series data
const seasonLengths = seasonalities(y);
const model = ets(seasonLengths, { impute: true });
model.fit(y);
const predictionInterval = 0.95;
// Generate in-sample predictions for the training set.
const { point, lower, upper } = model.predictInSample(predictionInterval);
// Generate out-of-sample forecasts.
const { point: futurePoint, lower: futureLower, upper: futureUpper } = model.predict(10, predictionInterval);
Troubleshooting
Webpack
Some of the dependencies of augurs
require a few changes to the Webpack configuration to work correctly.
Adding this to your webpack.config.js
should be enough:
{
experiments: {
// Required to load WASM modules.
asyncWebAssembly: true,
},
module: {
rules: [
{
test: /\@bsull\/augurs\/.*\.js$/,
resolve: {
fullySpecified: false
}
},
]
},
}
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