Nostradamus.js

-- is a time-series forecasting tool for Node.js

-- uses triple exponential smoothing via the Holt-Winters approach

-- works best with seasonal && trending data

-- can be quite useful in a machine context

-- is not maintained

---

Installation

$ npm install nostradamus

Usage

Option 1

// plain-vanilla
var forecast = require('nostradamus')
  , data = [
      362, 385, 432, 341, 382, 409,
      498, 387, 473, 513, 582, 474,
      544, 582, 681, 557, 628, 707,
      773, 592, 627, 725, 854, 661
    ]
  , alpha = 0.5  // overall smoothing component
  , beta = 0.4   // trend smoothing component
  , gamma = 0.6  // seasonal smoothing component
  , period = 4   // # of observations per season
  , m = 4        // # of future observations to forecast
  , predictions = [];

predictions = forecast(data, alpha, beta, gamma, period, m);
// -> [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 594.8043646513713, 357.12171044215734, ...]

Option 2

// faster w/ reuse of internal arrays
// if you know you'll be feeding it
// the same # of data, same params (alpha, beta, etc.),
// and you need to throw tons of data at it

var forecast = require('nostradamus')
  , data = [
      362, 385, 432, 341, 382, 409,
      498, 387, 473, 513, 582, 474,
      544, 582, 681, 557, 628, 707,
      773, 592, 627, 725, 854, 661
    ]
  , predictions = [];
  
forecast = forecast.memo({
  length: data.length,
  alpha: 0.5,  // overall smoothing component
  beta: 0.4,   // trend smoothing component
  gamma: 0.6,  // seasonal smoothing component
  period: 4,   // # of observations per season
  m: 4         // # of future observations to forecast
});

predictions = forecast(data);
// -> [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 594.8043646513713, 357.12171044215734, ...]

forecast([...]);
forecast([...]);
forecast([...]);
...

Rules

Some rules your parameters must abide by:

• alpha >= 0.0 && alpha <= 1.0
• beta >= 0.0 && beta <= 1.0
• gamma >= 0.0 && gamma <= 1.0
• m > 0
• m <= period

module.exports = function(data, alpha, beta, gamma, period, m)

• data {Array} series of input (numbers) from which a forecast should be made
• alpha {Number} overall Level component
• beta {Number} Trend component
• gamma {Number} Seasonal component
• period {Number} number of observations per 'season'
• m {Number} number of future observations to forecast

module.exports.memo = function(options)

• options {Object} options to memoize if forecasts follow a fixed format
  • length {Number} length of each frame of input (i.e. data.length)
  • alpha {Number} overall Level component
  • beta {Number} Trend component
  • gamma {Number} Seasonal component
  • period {Number} number of observations per 'season'
  • m {Number} number of future observations to forecast

---

Testing

In the main directory of this module: $ npm test

Credit

This project would't exist, if not for the versions written in Go and Java. Thanks!

License

MIT License

Copyright (c) 2012 - thick

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.