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Exponential smoother/forecaster with de-seasonalization
Smooth models an unobserved level and trend component in a noisy signal, along with optional "seasonal" effects at the level of day-over-day variation. The result is a smoothed version of the signal which may be used to forecast future values or detect unexpected variation.
In this example, we apply our smoother to a sample 10-day timeseries published in Twitter's AnomalyDetection package. The series is 10 days of counts, with a regular daily pattern and occasional surprises (spikes and dips). The Juttle output focuses on the three days around Oct 1.
hmm, this aint juttle. |
This is gist 1. |
info: Welcome to Nodejitsu alfred.sessionbox | |
info: jitsu v0.13.8, node v0.10.24 | |
info: It worked if it ends with Nodejitsu ok | |
info: Executing command deploy | |
info: Analyzing application dependencies in ./apps/run-sbapp.js | |
info: Creating snapshot 0.0.1-1 | |
info Uploading: [=============================] 100% | |
error: Error running command deploy | |
error: npm exited with code 34 | |
error: npm installation failed, please double check your package.json dependencies |