26medias / timeseries-analysis Goto Github PK

NPM Package - Timeseries analysis, noise removal, stats, ...

JavaScript 100.00%

timeseries-analysis's Issues

[feature] configure graph type

Hi,
It would be handy to be able to configure the output graph.
For example change the type of graph (example : bar).

coeffs [null,null,null,null,null]

Initialized t as var t = new timeseries.main(data); console.log(t)
Result is : {
"options": {},
"data": [
[
"2017-08-01T00:00:00.000Z",
"38.62"
],
[
"2017-07-01T00:00:00.000Z",
"38.96"
],
[
"2017-06-01T00:00:00.000Z",
"21.64"
],
[
"2017-05-01T00:00:00.000Z",
"22.96"
],
[
"2017-04-01T00:00:00.000Z",
"24.60"
],
[
"2017-03-01T00:00:00.000Z",
"25.87"
],
[
"2017-02-01T00:00:00.000Z",
"24.02"
],
[
"2017-01-01T00:00:00.000Z",
"22.47"
],
[
"2016-12-01T00:00:00.000Z",
"20.32"
],
[
"2016-11-01T00:00:00.000Z",
"18.26"
],
[
"2016-10-01T00:00:00.000Z",
"28.92"
],
[
"2016-09-01T00:00:00.000Z",
"28.19"
],
[
"2016-08-01T00:00:00.000Z",
"27.64"
],
[
"2016-07-01T00:00:00.000Z",
"26.99"
],
[
"2016-06-01T00:00:00.000Z",
"27.90"
],
[
"2016-05-01T00:00:00.000Z",
"31.42"
],
[
"2016-04-01T00:00:00.000Z",
"34.21"
],
[
"2016-03-01T00:00:00.000Z",
"34.73"
],
[
"2016-02-01T00:00:00.000Z",
"36.54"
],
[
"2016-01-01T00:00:00.000Z",
"38.06"
],
[
"2015-12-01T00:00:00.000Z",
"40.19"
],
[
"2015-11-01T00:00:00.000Z",
"38.33"
],
[
"2015-10-01T00:00:00.000Z",
"38.43"
],
[
"2015-09-01T00:00:00.000Z",
"36.88"
]
],
"original": [
[
"2017-08-01T00:00:00.000Z",
"38.62"
],
[
"2017-07-01T00:00:00.000Z",
"38.96"
],
[
"2017-06-01T00:00:00.000Z",
"21.64"
],
[
"2017-05-01T00:00:00.000Z",
"22.96"
],
[
"2017-04-01T00:00:00.000Z",
"24.60"
],
[
"2017-03-01T00:00:00.000Z",
"25.87"
],
[
"2017-02-01T00:00:00.000Z",
"24.02"
],
[
"2017-01-01T00:00:00.000Z",
"22.47"
],
[
"2016-12-01T00:00:00.000Z",
"20.32"
],
[
"2016-11-01T00:00:00.000Z",
"18.26"
],
[
"2016-10-01T00:00:00.000Z",
"28.92"
],
[
"2016-09-01T00:00:00.000Z",
"28.19"
],
[
"2016-08-01T00:00:00.000Z",
"27.64"
],
[
"2016-07-01T00:00:00.000Z",
"26.99"
],
[
"2016-06-01T00:00:00.000Z",
"27.90"
],
[
"2016-05-01T00:00:00.000Z",
"31.42"
],
[
"2016-04-01T00:00:00.000Z",
"34.21"
],
[
"2016-03-01T00:00:00.000Z",
"34.73"
],
[
"2016-02-01T00:00:00.000Z",
"36.54"
],
[
"2016-01-01T00:00:00.000Z",
"38.06"
],
[
"2015-12-01T00:00:00.000Z",
"40.19"
],
[
"2015-11-01T00:00:00.000Z",
"38.33"
],
[
"2015-10-01T00:00:00.000Z",
"38.43"
],
[
"2015-09-01T00:00:00.000Z",
"36.88"
]
],
"buffer": [],
"saved": []
}

Then declare coeffs as var coeffs = t.ARMaxEntropy({data: t.data}); and var coeffs = t.ARMaxEntropy();

In both cases coeffs is logged as [null,null,null,null,null]

From mongoose, get date from nested element

Is there a way to specify the date field in mongo/oose object, for instance I have a mongo object like:

{
    "_id" : ObjectId("5988797d39009be743441bfa"),
    "id" : 14227183,
    "timestamp" : {
        "time" : ISODate("2017-08-07T14:29:47.140Z")
    },
    "price" : 0.00586018,
}

from the example, I tried the following (see timestamp.time). Is something like that possible

var t     = new timeseries.main(timeseries.adapter.fromDB(data, {
    date:   'timestamp.time',
    value:  'price'
}));

Request to make forecasting new values feature work

can someone make the regression_forecast() method works to get n new forecasted values based on the overall sample data?

you can find this block of code below in the source code, but it broke and not fixed until now.
and why this block code "buffer[i][1] -= buffer[i-1-j][1]*coeffs[j];" only calculate last coeff.length data points? why not the entire sample data points? this led to linear values.

please someone help me to make it work as soon as possible because I truly need it.


timeseries.prototype.regression_forecast = function(options) {
	options = _.extend({
		method:		'ARMaxEntropy',	// ARMaxEntropy | ARLeastSquare
		sample:		50,		// points int he sample
		start:		100,	// Where to start
		n:			5,		// How many points to forecast
		degree:		5
	},options);
	
	var i;
	var j;
	var l = this.data.length;
	
	var mean	= this.mean();
	this.offset(-mean);
	var backup 	= this.clone();
	var buffer 	= this.clone();
	
	var sample 		= buffer.slice(options.start-1-options.sample, options.start);
	
	// The current data to process is only a sample of the real data.
	this.data		= sample;
	// Get the AR coeffs
	var coeffs 		= this[options.method]({degree: options.degree});
	console.log("coeffs",coeffs);
	
	for (i=options.start;i<options.start+options.n;i++) {
		buffer[i][1]	= 0;
		for (j=0;j<coeffs.length;j++) {
			if (options.method == 'ARMaxEntropy') {
				buffer[i][1] -= buffer[i-1-j][1]*coeffs[j];
			} else {
				buffer[i][1] += buffer[i-1-j][1]*coeffs[j];
			}
		}
		console.log("buffer["+i+"][1]",buffer[i][1]);
	}
	this.data = buffer;
	this.offset(mean);
	
	return this;
}

Forecasting Data

I've a data set of 120 items i need to forecast next item or next 5 items how to do it.

I've tried below example
`var forecastDatapoint = 121;

var coeffs = t.ARMaxEntropy({
    data:	t.data.slice(0,120)
});
console.log(coeffs);
 

var forecast	= 0;	// Init the value at 0.
for (var i=0;i<coeffs.length;i++) {	// Loop through the coefficients
    forecast -= t.data[10-i][1]*coeffs[i];
}
console.log("forecast",forecast);`

I have noticed that you've no where used forecastDatapoint this variable in above example so how exactly you're calculating 121 (11 in example) data point
also tired the sliding_regression_forecast but it failed to show red dot indicate at which point the forecast starts.
can you help in forecasting next 5 data points?

coeffs NaN

Hey there.

I am successfully running this to do some forecast on some data samples i periodically store.

However I just tried to do the same code that works on larger dataset (60+ datapoints) on a small dataset (8 datapoints) and the coeffs that I calculate like so:

var coeffs = t.ARMaxEntropy({
data: t.data
});

contains [NaN, NaN, NaN, NaN, NaN]

Why is that? is it because I use 5 coeffs and my dataset is too small?

Returning NAN after length of indicator.

const dataForge = require('data-forge');
require('data-forge-fs');
require('data-forge-plot');
const timeseries = require("timeseries-analysis");


const df = dataForge.readFileSync('binance_btc_usdt_time_5m.csv')
    .parseCSV()
    .dropSeries(['id', 'open', 'high', 'low', 'volume', 'startTime', 'endTime', 'tradeId']) // Drop certain columns
    
const newDfArray = df.toArray()

// Load the data
const t     = new timeseries.main(newDfArray);
const trend = t.lwma({
    period:    6
});

// returns https://chart.googleapis.com/chart?cht=lc&chs=800x200&chxt=y&chd=s:JDOLhghn0s92xuilnptvxz1110zzzyyvrlgZUPMHA&chco=76a4fb&chm=&chds=63.13,70.78&chxr=0,63.13,70.78,10
console.log(t.ma().output())

Trying to use your script. Its returning this for t.ma().output();

[ { close: '4261.48' },
  { close: '4266.29' },
  { close: '4261.45' },
  { close: '4296.63' },
  { close: '4300.38' },
  { close: '4310.07' },
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
  [ undefined, NaN ],
etc....

Any clue why?

Forecasting data form existing data set.

have a question regarding forecasting. I have a dataset with timestamp and value for the last 10 items and I want to forecast next 5 items, how i can do that?

Getting forecasted point

Hi, I'm having trouble forecasting using this example of the docs:

let bestSettings = t.regression_forecast_optimize();
      t.sliding_regression_forecast({
        sample: bestSettings.sample,
        degree: bestSettings.degree,
        method: bestSettings.method
      });

How can I easily get the length + 1 point in a var ?

Thanks!

Pure Javascript implementation?

Is it possible to use this in front end? Are you aware of any similar library?

Timeseries Analysis Version 2

https://github.com/pre-vail/timeseries-analysis-v2
I made a version 2 of this package. It has not been endorsed by Julien Loutre.

Method author?

Hello!
Please, tell me who is the author or what is the "official" name of the "Iterative Noise Removal" method.
Or may be it is the modification of other method?

With best regards,
Dmitry.

google chart url broken

When retrieving the graph url, I get something as complicated as:

https://chart.googleapis.com/chart?cht=lc&chs=800x200&chxt=y&chd=s:87777777887777777777788777777777777777777777777777777777777777777777777777777777777778888888888887777777777777766666666667777777777777777777777777777788888888888888888999999999999999999999999999999999999999999999999998888888888888888888888888888888888887777777777777777777777777777777778888888888888888888888888777777666655554444444444444444555555666666777777888888888888888777776666555444333333222223333333333333333333334444444444444444444443333333333333333333333333333333333333333333333322222222222222222222222222222222222223333333333333333322222222222222222222222222211111111111111111111100000000000zzzzzzzzzzz00000000000000011111111222222222222222222222222222222222222222111111111000000111112222222222222221111000000zzzzzzzzzzzzzzzzzzzzzzzzzzzyyyyyyyyyyyyxxxxxxwwwwwwwwwvvvvvvvvvvvvvvvvvvwwwwwwwwwwwwwwwwwwwwwwxxxxxxxxyyyyzzz00001111111111111111111111111111222222222222223333333333333333333333333333333333333333444444555555555566655555556666666666666666666777777777777777777777777777777777788888888888888888888888888888888888888888888888888888888888888888887777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777776666666555443332211100000000000000011111222222333333334444433333322222111111100000000000000000000000000000000000000zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz0000000000000000zzzzzzzzzzzzzzzzzzzyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyzzzzzzzzzzzzzzzzzzzzzzz0000000000000000000000000001111111111111111111111111111111111222222233333333444444444555555555555555555555666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666655555555555555555555555555544444444444444444444444444444444444444444444444444444444444444444444444444433333333333344444444444444443333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333334444444444444444443333333333333333333333333333333333333333333333333222222222222222222222222222222222222222222222222222222222222222222222222222222222222222211111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111000000000011111111111122222222111111111111111111111111111000000000000000000000000000000000000zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzyyyyyyxxxxxxwwwwwwwwwwwxxxwwwwwwwwvvvvvuuuuuuuuuuuuuuuuuvvuuuuuuuuuuuuttttttttttttttsssssssssrrrrrrrrrrsssssssssssssssssssrrrrqqqqpppooooonnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnoooooooooooopppppppppppppooooooonnnnnmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmlllllllllllkkkkkjjjjjjjjiiiiiiiiiiiiihhhhhggggffffffeeeeeeeeeeffffffffffggggggggggggggggggghhhhhhhhhhhhhhhhhhhiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiijjjjjjkkkkkkkkkkkkkkkkkkkkkkkklllllmmmmnnnnnnnnnooooooooooooooooooooooooooooooooooppppqqqrrrsssssssssssssssssrrrrrrrrsssssstttttttuuuuuuuuuutttttttsssrrrrqqqqppppppppppqqqqqrrrrrrrrrrrrrrrrrqqqqpppooonnnnnnnmmmmmmmmmmmllllkkkjjjiiiihhhggggfffeeeeeeeeeeeeeeffffgggghhhiiiijjjjjjjjjjiiiihhhhhhhhhhhhhhhiiiiiiijjjjiiiiiiihhhhhhggggggggghhhhhhhiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiihhhhhhhhhhhhhiiiiiiiiiiiiiiiiiiiiiiiiiihhhhhhhhhhhhhiiiiiiiiiiiiiiiiiiiiihhhhhhhhhggggggggggggggggggggggggggggggggggggggggggggffffffffffffffffffffffffffffffffffffffffffggggggggggggggggggggghhhhgggggggggggggggggffffffffffffgggggggggggggggggggggggggggggggffffffffffffffffffffffffffffffffffeeeeeeeeeeeeddddddddddddddddeeeeeeeeeeeeeeeeeeeddddddddcccccbbbbbbaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaabbbbbbbbbbbbbbcccccccbbbbbbbaaaaaaZZZZZZZZZZZZZZZZZZZZZYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXWWWWWWWWWWWVVVVVVVVVVVVVVVVVUUUUUUUUUUUUUUUUUUUUUUUUUUVVVVVVVVVVVVVUUUUUUUUTTTTSSSRRRQQQPPPPOOOOONNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNOOOOOOOOOOPPPPPPPPPQQQQQQQQRRRRRRRRSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTSSSSSSSSRRRRRRSSSSSSSRRRRRQQQPPPOOONNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNMMMMMMMMLLLLLKKKKKJJJIIIHHHGGFFEEEDDCCCBBBBBAAAAAAAAAAAAAAAABBBBBBBBBBBCCCCCCCCCCDDDDDDDDDEEEEEEEEFFFFFFFFGGGGGGGGGGGGHHHHHHHHHHHHHIIIIIIIIIJJJJJJJJKKKKKKKKKLLLLLLLLLLLKKKKKKJJJJIIIIIIIIIIIIIJJJJJKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKJJJJJJJJJJJJJJJJJJKKKKKKKKJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJKKKKKKKKKKKKKKKKKKKKKKKKKKKKKLLLMNNOPQRSTUVWXYZaabbbbbbbbbbaaaZZZYYYYXXXXWWWWWWVVVVUUUUTTTSSRRRQQPPPOOONNNNNNNNNOOPPQRSTUVWXYZabccddeeeeeeeeeeeeeeeeeeeeeeeeeeffffggghhiiiijjjjjjjjjkkkkkkkkkkkkkkjjjjjiiiiihhhhhhhhhhhhhhhiiiiiiiiihhhhhhhhhggggggggggggggggggghhhhhhhhhhhhhhhhhhhhhhhhggggffffeeeeeeeeeeeeeeffffffffffffffffffffffffffffeeeeeeeeeedddddccccbbbbaaaaaaaZZZZZZZZZZZZZZZZZZZZZZYYYYXXWWVUUTSRQPONMMLKKKJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJIIIIIIIIIIIIIJJJJJJJJJJJJJJJJJJJKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLKKKKKKJJJJJIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIJJJJJJJKKKKKKKKKKKKJJJJJJJJJJJIIIIIIJJJJKKLMMNOPRSTUVXYZZabbbccccbbbbaaaZZZZYYYYYXXXXXXWWWWWVVVVVUUUUUUUTTTTTTTTTUUUVVWWWXXXYYYYYYYYYYYYYZZZZZaaaaabbbbbbbbbcccccdddddddeeedddddccbbaaZYXXWVUTTSRRQQQQQQQRRSSTTUVVWXXYYZZZZZZZZZYYXXWWVVUUUTTTTTTTTTTTTTUUUUUUUUUUUUUUUUUUUUUUUUUUTTTTTTTTTTTTTTTTTTTTTTUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUTTTTTTSSSSRRRQQQPPPOOONNNNNNNNNNNNNNOOOOOOPPPPPPPPPPPPPPPPPPPPPPPPQQQQQQRRRRRSSSSTTTTUUUUUVVVVVVVVVVVVVVVVVVVVVVVUUUUUUUUUUUUUUUUUUUUUUUUUUUUUVVVVVVVVVVVVUUUUUUUTTTTTTTTUUUUUUUUUVVVVVVVVVVVVVVWWWWWWWWWWWWWWWVVVWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWXXXXXXXXXXXXXXXXXXXXXXXXYYYYYYYYYYYYYZZZZZZZZZZZZZZZYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYXXXXXXWWWWWWWWWWWWWWWWWXXXXXXWWWWWWVVVVVUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUVVVVVWWWXXXYYYZZZZZZZZZZZZZYYYYYYXXXXXXXXXXYYYYZZZZZaaaaaaZZZZZZZZZZZZZZaaaaaaZZZZZZZZZZZZZZZZZZZZZZaaaaaaaaaaaaaabbbbbbbbbbbbbbcccccccccccccccccccddddddddddeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffeeeeeeddddccccccccccccccccccccdddddeeeeeeeeeeeddddcccbbbaaaaZZZZZZZZZZYYYYYYYYYYYYXXXYYYYYYZZZaaabbbcccdddeeefffffffgggfffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffeeeeeeeedddddddddcccccccccccccccbbbbbbbaaaaZZZZZZZZZZZZZZZZZZZZZZYYYYYXXXXWWWWWWWWWWWWWWWXXXXXXXXWWWWWWWWWWWWWWWWWWWWWWWWWVVVVVVVVVVVVWWWWWWWWWWWWWWWWWWWWWWWWWVVVVVVVVVVVVVVVVVVVUUUUUUUUUUUUUUUUUUUUVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVUUUUUUUTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUUUUTTTTTTTSSSRRRQQQPPPPPOOOOOOOOOOPPPPPPPPPPPPPPPPPPPPOOOOOOOOOOOOOOOOPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPQQQQQQQQQQQQQQQQRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRQQQQQQQQQQQQQQQQQQQQQQQQQQPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPQQQQQQQQQQQQQQQQQQQQQQQQQQQQRRRRRRRRRRRRRRRRRRRRRSSSSSTTTTTTTTTTTTTTTTTTTSSSSSSSTTTTTTTUUUUUVVVVVVVVVVVVVVVVVVVVVVVVVVVUUUUUUUUUUUUUUUUUUUUUUUTTTTTTSSSSSSSSTTTTTTUUUUUUUVVVVVVVWWWWWWWWXXXXXYYYYYYYYZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZaaaaaaaaaaaaaaabbbbbbbbbbbbbbbbbcccccccccccccccccccccccccccddddddddddeeeeeeeeeeeeeeddddddeeeeeeeeefffffffffggggggggggggggffffffgggggggggggggggggggggggggggggggghhhhhhhhhgggggggggggggggggggggggggffffffffffffgggggggggggggggggffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeedddddddddddddddddddddddddddddddddddcccccccdddddddddddddddddddddddddddddddddddddddccccbbbaaaaZZZZZZYYYYYZZZZZZZZZZaaaaaaaaaZZZZZZZYYYYYYYXXXXXXXXXXXXXXXXWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWVVVVVVVVVVVVVVVVVVVVVVVVVWWWWWWWWWWWWWWWWWWWWWWWWWWWWWXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXWWWWWWWWWWWVVVVVVVVVVVVVWWWWWWWWWWWXXXXXXXXXXXXXXXXXWWWWWWWWWWWWWWWWWWWWWXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXWWWWWVVVUUTTTSSRRRRQQQQQQQQQQQRRRRRRRSSSSSSTTTTTUUUUUUVVVVVVVVVVVVVVVVWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWVVWWWWWWWWWWWWWWWWWXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX&chco=76a4fb&chm=&chds=0.00540111450504935,0.005875491446632071&chxr=0,0.00540111450504935,0.005875491446632071,10

That gives:

However, when I then paste that back into my terminal (shell) - which I did by mistake the first time, I get a shorter url:

https://chart.googleapis.com/chart?cht=lc&chs=800x200&chxt=y&chd=s:87777777887777777777788777777777777777777777777777777777777777777777777777777777777778888888888887777777777777766666666667777777777777777777777777777788888888888888888999999999999999999999999999999999999999999999999998888888888888888888888888888888888887777777777777777777777777777777778888888888888888888888888777777666655554444444444444444555555666666777777888888888888888777776666555444333333222223333333333333333333334444444444444444444443333333333333333333333333333333333333333333333322222222222222222222222222222222222223333333333333333322222222222222222222222222211111111111111111111100000000000zzzzzzzzzzz00000000000000011111111222222222222222222222222222222222222222111111111000000111112222222222222221111000000zzzzzzzzzzzzzzzzzzzzzzzzzzzyyyyyyyyyyyyxxxxxxwwwwwwwwwvvvvvvvvvvvvvvvvvvwwwwwwwwwwwwwwwwwwwwwwxxxxxxxxyyyyzzz00001111111111111111111111111111222222222222223333333333333333333333333333333333333333444444555555555566655555556666666666666666

which in turn, does produce a google chart:

any ideas whats going on there? and why i cant get the url first time round, but some weird hocuspocus is occuring when pasting back into terminal?

Thanks

anyupdates

Are there any major updates to this model?

Feature request: MA for time period even if data is missing

First of all, thanks a lot for this nice share. I see you have put some good thought on the forecast functions and so on.
You library can be handy but there is one important feature missing.
Let's say I have incomplete data, for example, I have one value every one minute, but for 10 minutes I have missing data. If I use the MA function, for a period of 60, the output will be wrong, because the algorithm considers that the data is complete.

Do you know a little trick to detect missing data? We would need to consider the dates in that case and not only the values...

Reference for smoother()

I really like this package! Currently, I am employing smoother() for smoothing data that will be presented in a research paper. For this purpose, it would be great to quote a reference for the algorithm that is implemented by smoother(). Does there exist any reference for it?

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.