Registry / ai-ml / node-red-contrib-industrial-machine-forecast

node-red-contrib-industrial-machine-forecast

library1.0.2jsnpmunverified

Node-RED custom node for time-series forecasting and anomaly detection using exponential smoothing. Current version 1.0.2, stable release. It forecasts the next value in a time series based on a configurable smoothing factor (alpha) and detects anomalies (sudden spikes/drops) using a running estimate of forecast error variance and a threshold in standard deviations. Offers an adaptive mode that adjusts alpha based on data volatility. Unlike general statistical libraries, this is specifically designed as a Node-RED node with status indicator and configurable settings, intended for predictive maintenance and real-time monitoring. Released under GPL-3.0.

ai-ml data devops

npm install node-red-contrib-industrial-machine-forecast

github ↗package ↗

INSTALL

IMPORT

SIG · NODE-RED-CONTRIB-I

node-red-contrib-industrial-machine-forecast

ai-mljavascriptv1.0.2

Install

—

Import

—

Disk

—

Pass rate

0/ 6

Env Coverage0 / 6

glibc

18–22

musl

18–22

Install & Compatibility

Where this runs

tested against v? · npm install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

node 18–226 runs

build_error

glibc

node 18–226 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

default (node)

✓ Install via npm and then use in Node-RED flow; no direct import in JavaScript/TypeScript.

This is a Node-RED custom node; you install it as a package via npm, then drag the node from the palette in the Node-RED editor. There is no programmatic import in typical JS/TS code.

Node-RED node usage: install via npm, configure alpha and threshold, pass numeric payload to detect anomalies.

// This package is a Node-RED node, not a library for programmatic use. // To use it, install Node-RED first, then: // npm install node-red-contrib-industrial-machine-forecast // Then in Node-RED flow editor, add it from the palette. // Input: msg.payload = numeric observation // Output: msg.payload = { value: number, status: "Normal" | "Anomaly - Sudden Spike" | "Anomaly - Sudden Drop" }

Debug

Known issues

gotchaNode expects numeric payload; non-numeric values cause silent failure or errors.

fix

Ensure msg.payload is a number before sending to the node (e.g., use a Change node to convert).

affects: >=1.0.0

gotchaNode maintains state between messages; adapter mode changes alpha over time, which may cause inconsistent behavior if node is restarted or deployed.

fix

Be aware that the node uses in-memory state; redeploying the flow resets the state. For persistent state, consider using a context store.

affects: >=1.0.0

gotchaAnomaly detection threshold is static unless adaptive mode modifies alpha; variance estimate can be affected by sequence length.

fix

Tune the Std Devs threshold based on expected data variability. Start with 2-3 standard deviations and adjust.

affects: >=1.0.0

Errors

Common errors & fixes

Error: [node-red-contrib-industrial-machine-forecast] Invalid payload: must be a number

msg.payload is not a numeric value (e.g., string, null).

fix

Use a Change node to set msg.payload to a number, e.g., msg.payload = Number(msg.payload).

TypeError: Cannot read properties of undefined (reading 'at')

Node is missing initial state (e.g., first message after deploy without prior data).

fix

Ensure the first message sent to the node is a valid numeric payload. The node will initialize its state from the first message.

Upgrade

Version history

1.0.2latest on npm

Audit

Dependencies

No dependency data recorded yet.

Agent activity

2 hits · last 30 days

node

Resources

githubhttps://github.com/hj91/node-red-contrib-industrial-machine-forecast ↗

packagehttps://www.npmjs.com/package/node-red-contrib-industrial-machine-forecast ↗