chronology

v1.0.2

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9 months ago

A flexible library for representing and manipulating time series data.

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json time series timeseries

Chronology.js

A flexible library for representing and manipulating time series data in Javascript. Runs in Node.js and the browser.

Create, query, and manipulate regular and irregular time series
Observations can hold any type of Javascript value
Highly configurable repetitive structure for regular time series
Serialize and unserialize for storage
Arbitrarily high frequencies for regular time series
Millisecond precision for irregular time series
Dense and sparse data represented efficiently
Running time increases roughly linearly with observations
Easily extensible
Well documented and thoroughly tested

Chronology.js

Getting Started

Installing the Package

NPM

Install the NPM package:

npm install chronology

Use the module:

const chronology = require("chronology");

// Create and iterate over a regular time series
const rts = chronology.regular({ basePeriod: [1, "y"] });
rts.period(new Date("2000-01-01Z")).obs.set(1)
  .forward().obs.set(2)
  .forward().obs.set(3);
rts.each(function(rp) {
  console.log(
    rp.start().toISOString(),
    rp.end().toISOString(),
    rp.obs.value()
  );
});

// Create and iterate over an irregular time series
const its = chronology.irregular();
its.add(new Date("2000-01-01Z"), 1, new Date("2000-06-30"));
      .add(new Date("2000-06-30Z"), 2, new Date("2000-07-15"))
      .add(new Date("2000-08-01Z"), 3, new Date("2001-02-01"));
its.each(function(ip) {
  console.log(
    ip.start().toISOString(),
    ip.end().toISOString(),
    ip.obs.value()
  );
});

CDN

The browser bundle can be included in a website as follows:

<script
  type="text/javascript"
  src="https://cdn.jsdelivr.net/npm/chronology"
></script>

The module is bundled in UMD format and is named chronology in the global scope.

JSON-TimeSeries Primer

This library implements the JSON-TimeSeries format for representing time series data. The format is relatively straight-forward and its specification is recommended reading for library users.

Under the JSON-TimeSeries format, time is thought of as continuous and dates are understood to refer to instants in time, as opposed to intervals of time. In the JSON-TimeSeries format and in this library, intervals of time are represented by a start date and an end date. An interval is considered to include the instant referenced by its start date, but not the instant referenced by its end date, which may be the start of a subsequent interval.

The JSON-TimeSeries format can represent regular time series of arbitrarily high frequency and irregular time series with observation boundaries of arbitrarily high precision. Unfortunately, since Javascript's date system operates only at millisecond-level granularity, this library is subject to more stringent limitations. In particular...

Regular time series: To make use of all library functions, regular time series must have at most millisecond frequency. Higher-frequency time series can be represented using the JSON-TimeSeries concept of sub periods, but when working with such series, sub periods cannot be referenced by date and sub period boundaries cannot be described using dates.
Irregular time series: Boundaries between observations can be specified only to millisecond precision.

When interacting with this library, Date objects should be understood to represent the instant at the start of the specified millisecond, not the entire millisecond. Microseconds and more granular date elements should be understood to be exactly 0.

Regular Time Series API

Observations in a regular time series occur over consistently-spaced intervals of time. Applications interact with regular time series using RegularSeries and RegularPeriod objects.

RegularSeries Objects

Initialization

To create a new RegularSeries object representing a regular time series:

const rts = chronology.regular(options);

The options argument is an object with the following properties:

options.basePeriod - Required Array specifying a JSON-TimeSeries base period.
Takes the form [basePeriodNumber, basePeriodType].
The basePeriodNumber (positive integer) indicates the number of basePeriodTypes that constitute a base period. For example, a two-week base period is specified using [2, "w"].
The basePeriodType (string) must be one of:
- y - Year
- m - Month
- w - Week
- d - Day
- h - Hour
- n - Minute
- s - Second
- ms - Millisecond
- e-3 - Millisecond
Higher frequencies are not supported as base periods, as Javascript dates operate only at millisecond precision. However, higher frequencies can be achieved using numerically indexed sub periods.
options.anchor - Optional Date specifying the JSON-TimeSeries anchor.
Defaults to Monday at midnight UTC for weekly base periods and January 1st at midnight UTC for all other base periods.
options.subPeriods - Optional positive integer specifying the number of JSON-TimeSeries sub periods within each base period.
Sub periods can be used to create time series of arbitrarily high frequency. For example, microsecond frequency can be achieved by setting basePeriod to ms and subPeriods to 1000.
Defaults to 1.
options.subPeriodBoundaries - Optional function that specifies how to split base periods into sub periods in terms of calendar time. The JSON-TimeSeries format prescribes base period boundaries, but leaves sub period boundary determination to the library/application.
If subPeriodBoundaries is omitted, the library will split base periods into sub periods of equal length, calculated to the millisecond.
The subPeriodBoundaries function is called within the context of the time series object (ie this refers to rts) and is passed three arguments:
1. basePeriodStart - A Date referencing the start of the base period (inclusive).
2. basePeriodEnd - A Date referencing the end of the base period (exclusive).
3. subPeriod - A positive integer between 1 and subPeriods indicating the sub period whose boundaries are being requested.
Unless a precision issue arises (details below), the subPeriodBoundaries function must return an object containing two properties:
1. start - A Date indicating the calendar start of the sub period. The instant represented by start is considered part of the sub period. The start date must be later than or equal to the start of the base period and strictly earlier than the end of the base period.
2. end - A Date indicating the end of the sub period. The time leading up to end is considered part of the sub period, but the actual instant represented by end is considered part of the next sub period. The end date must be strictly later than start and earlier than or equal to the end of the base period.
For a given base period, the subPeriodBoundaries function must return non-overlapping calendar boundaries for all sub periods. That is, the start of each sub period must be later than or equal to the end of the previous sub period.
The subPeriodBoundaries function is not required to allocate the entire base period to sub periods. For example, a business day time series would have a base period of [1, "w"] and five sub periods representing Monday through Friday. In that case, Saturdays and Sundays would not correspond to sub periods.
Since start and end are Javascript Date objects, the subPeriodBoundaries function can only describe boundaries to millisecond precision. If the overall frequency determined by options.basePeriod and options.subPeriods implies a shorter-than-millisecond duration for sub periods, then the library will not call subPeriodBoundaries. The subPeriodBoundaries function may also return {start: null, end: null} if Javascript dates are unable to represent sub period start and end dates to sufficient precision. In such cases, the application must reference sub periods by number.
The subPeriodBoundaries function is subjected to a series of tests on initialization, which attempt to detect invalid behavior.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.

Methods

rts.period(date)

Returns a RegularPeriod object referencing the base/sub period at the specified date.

Arguments:

date (Date) references any time within the base/sub period.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.
err.message === "UNALLOCATED_DATE: ..."
The options.subPeriodBoundaries function does not allocate date to any sub period.
err.message === "INSUFFICIENT_PRECISION: ..."
Sub period boundaries could not be determined due to the precision limitations inherent in Javascript dates. Use rts.period(basePeriodDate, subPeriod) instead.

rts.period(basePeriodDate, subPeriod)

Returns a RegularPeriod object referencing the specified base/sub period.

Arguments:

basePeriodDate (Date) references any time within the base period.
subPeriod (number) indicates the number of the sub period to retrieve. Sub periods are indexed from 1 to options.subPeriods.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.

rts.count()

Returns a number indicating the number of observations in the series.

Errors thrown: None

rts.first()

Returns a RegularPeriod object referencing the earliest period with an observation.

Errors thrown:

err.message === "MISSING: ..."
The time series has no observations.

rts.last()

Returns a RegularPeriod object referencing the latest period with an observation.

Errors thrown:

err.message === "MISSING: ..."
The time series has no observations.

rts.each(fn)

Iterates chronologically through each observation in the time series calling fn(rp), where rp is a RegularPeriod object.

Iteration is performed using rp.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Returns a reference to rts.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.eachPeriod(fn)

Iterates chronologically from the earliest to the latest observation in the time series calling fn(rp) on all intervening periods, where rp is a RegularPeriod object.

Iteration is performed using rp.forward() after each call to fn, so time series modifications by fn are reflected.

Returns a reference to rts.

Arguments:

fn (Function) is called once for each period as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.map(fn)

Returns a new RegularSeries object constructed by iterating through all observations in rts and calling fn(v), where v is the observation value, and assigning the return value to the same period in the new time series.

Iteration is performed using rp.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.transform(fn)

Returns a new RegularSeries constructed by iterating through all observations in rts and calling fn(rpExisting, rpNew), where rpExisting is a RegularPeriod referencing the observation in rts and rpNew is a RegularPeriod referencing the same period in the new time series. fn may add observations to the new time series using rpNew.obs.set().

Iteration is performed using rp.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.reduce(fn, initialAccumulator)

Iterates chronologically over each observation in rts calling fn(accumulator, rp), where accumulator is the current accumulator value and rp is a RegularPeriod object referencing the current observation.

For the first observation, fn is passed initialAccumulator as the accumulator value. For subsequent observations, fn is passed the return value of the previous call to fn as the accumulator value.

The value returned by fn for the final observation is returned as the overall method result. The return type is therefore determined by the application.

If there are no observations in rts then initialAccumulator is returned.

Iteration is performed using rp.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.
initialAccumulator (any type) is passed as an argument with the first call to fn.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.filter(fn)

Returns a new RegularSeries created by iterating chronologically over each observation in rts and calling fn(rp), where rp is a RegularPeriod referencing the observation. If fn returns truthy, the observation is included in the new series, otherwise it is omitted.

Iteration is performed using rp.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

rts.subSeries(rpStart, rpEnd)

Returns a new RegularSeries containing observations between rpStart and rpEnd, inclusive.

Arguments:

rpStart (RegularPeriod) indicates the startperiod for the sub series.
rpEnd (RegularPeriod) indicates the end period for the sub series.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

rts.overlay(rts2)

Returns a new RegularSeries object with observations from both rts rts2. If rts and rts2 both contain an observation for a given period, the observation in rts2 takes precedence.

Arguments:

rts2 (RegularSeries) is the time series to be overlaid on rts.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

rts.clone()

Returns a new RegularSeries object with the same observations as rts.

Errors thrown: None

rts.serialize()

Returns a string JSON-TimeSeries serialization of the time series. Dates in the serialization are expressed in UTC.

Observation values must be JSON-serializable using JSON.stringify().

Errors thrown:

err.message === "NOT_SERIALIZABLE: ..."
The time series could not be serialized due to the presence of an undefined value, a NaN value, or a cyclical structure in one or more observations.

rts.reset()

Clears all observations and returns a reference to rts.

Errors thrown: None

rts.type()

Returns "regular".

Errors thrown: None

rts.basePeriod()
Returns an Array with the JSON-TimeSeries base period. Takes the form [basePeriodNumber, basePeriodType].
Errors thrown: None

rts.anchor()

Returns a Date containing the JSON-TimeSeries anchor.

Errors thrown: None

rts.subPeriods()

Returns a number with the number of JSON-TimeSeries sub periods.

Errors thrown: None

RegularPeriod Objects

Time periods in regular time series are represented using RegularPeriod objects, which allow applications to navigate and modify observations. A RegularPeriod represents a slot for an observation, but may not actually hold and observation.

Observations are stored in the RegularSeries object, so modifications made to a time series through one RegularPeriod object are immediately reflected in all other RegularPeriod objects associated with that time series.

Methods

rp.series()

Returns a reference to the associated RegularSeries object.

Errors thrown: None

rp.start()

Returns a Date indicating the start of the period, which is calculated using the options.subPeriodBoundaries function. The instant represented is considered part of the period.

Returns null if the effective frequency of the time series is too high for sub period boundaries to be represented using Javascript dates.

Errors thrown: None

rp.end()

Returns a Date indicating the end of the period, which is calculated using the options.subPeriodBoundaries function. The instant represented is not considered part of the current period.

Returns null if the effective frequency of the time series is too high for sub period boundaries to be represented using Javascript dates.

Errors thrown: None

rp.basePeriodStart()

Returns a Date indicating the start of the base period. The instant represented is considered part of the base period.

Errors thrown: None

rp.basePeriodEnd()

Returns a Date indicating the end of the base period. The instant represented is not considered part of the current base period.

Errors thrown: None

rp.subPeriod()

Returns a number indicating the current sub period within the base period.

Errors thrown: None

rp.index()

Returns a number indicating the distance of the current period from the anchor date, measured in the number of sub periods.

Errors thrown: None

rp.forward(num)

Returns a new RegularPeriod object referencing a later period in the time series, which may or may not contain an observation.

Arguments:

num (number) is the number of periods to advance. Defaults to 1. A negative number can be supplied to retrieve earlier periods in the time series.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

rp.back(num)

Returns a new RegularPeriod object referencing an earlier period in the time series, which may or may not contain an observation.

Arguments:

num (number) is the number of periods to reverse. Defaults to 1. A negative number can be supplied to retrieve later periods in the time series.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

rp.obs.exists()

Returns true if the period contains an observation and false otherwise.

Errors thrown: None

rp.obs.set(value)

Sets the observation value for the time period and returns a reference to rp.

Arguments:

value (any type) is the value of the observation. Both null and undefined are valid observation values.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more supplied arguments.

rp.obs.clear()

Clears the observation value for the time period and returns a reference to rp. Returns success whether or not the period contains an observation.

Errors thrown: None

rp.obs.value()

Returns the observation value for the time period.

Errors thrown:

err.message === "MISSING: ..."
There is no observation assigned to the period.

rp.obs.hasForward()

Returns true if there is a later period in the time series with an observation and false otherwise.

Errors thrown: None

rp.obs.forward()

Returns a new RegularPeriod referencing the next period in the time series that contains an observation.

Errors thrown:

err.message === "MISSING: ..."
There is no later period with an observation.

rp.obs.hasBack()

Returns true if there is an earlier period in the time series with an observation and false otherwise.

Errors thrown: None

rp.obs.back()

Returns a new RegularPeriod referencing the previous period in the time series that contains an observation.

Errors thrown:

err.message === "MISSING: ..."
There is no earlier period with an obseration.

Examples

Initialize, populate, and display a quarterly time series with an October 31 year-end:

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "q"],
  anchor: new Date("2000-11-01")
});

// Populate
var period = rts.period(new Date("2000-11-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Initialize, populate, and display a semi-monthly time series:

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "m"],
  subPeriods: 2
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Initialize, populate, and display a business day (Monday-Friday) time series:

// Initialize
var msPerDay = 24 * 60 * 60 * 1000;
var rts = chronology.regular({
  basePeriod: [1, "w"],
  subPeriods: 5,
  subPeriodBoundaries: function(bpStart, bpEnd, spNum) {
    return {
      start: new Date(bpStart.getTime() + (spNum - 1) * msPerDay),
      end: new Date(bpStart.getTime() + (spNum) * msPerDay)
    };
  }
});

// Populate
var period = rts.period(new Date("2000-01-03")); // Monday
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Initialize, populate, and display a microsecond-frequency time series using sub periods:

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "ms"],
  subPeriods: 1000
});

// Populate
var period = rts.period(new Date("2000-01-01T00:00:00.000"), 995);
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display
rts.each(function(rp) {
  console.log(rp.basePeriodStart(), rp.basePeriodEnd(), rp.subPeriod(), rp.obs.value());
});

Increment all observation values by 10 using rts.map():

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display original
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

// Generate an incremented series
var rts2 = rts.map(function (val) { return val + 10; });

// Display new
rts2.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Generate a lagged time series using rts.transform():

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display original
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

// Generate lagged series
var rts2 = rts.transform(function (rpExisting, rpNew) {
  rpNew.forward().obs.set(
    rpExisting.obs.value()
  );
});

// Display new
rts2.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Sum observation values using rts.reduce():

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Sum and display
var sum = rts.reduce(function (accum, rp) {
  return accum + rp.obs.value();
}, 0);
console.log(sum);

Retrieve observations with negative values using rts.filter():

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = -5; i < 5; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display original
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

// Generate a filtered series
var rts2 = rts.filter(function (rp) {
  return rp.obs.value() < 0;
});

// Display new
rts2.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Serialize and unserialize a regular time series:

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = -5; i < 5; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Display original
rts.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

// Serialize and display
var ser = rts.serialize();
console.log(ser);

// Unserialize and display
var rts2 = chronology.unserialize(ser);
rts2.each(function(rp) {
  console.log(rp.start(), rp.end(), rp.obs.value());
});

Irregular Time Series API

Observations in an irregular time series may occur over inconsistently-spaced intervals of time. Applications interact with irregular time series using IrregularSeries and IrregularPeriod objects.

IrregularSeries Objects

Initialization

To create a new IrregularSeries object representing an irregular time series:

var its = chronology.irregular();

Errors thrown: None

Methods

its.period(date)

Returns an IrregularPeriod object referencing date. It is not required that there be an observation spanning date.

Arguments:

date (Date) specifies the date to be referenced by the IrregularPeriod object.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.

its.add(start, value, end)

Inserts a new observation into the time series and returns a reference to its.

Arguments:

start (Date) indicates the start of the time period to which the observation value applies. The specified instant is considered part of the observation.
value (any type) is the value of the observation. Both null and undefined are valid observation values.
end (Date) indicates the end of the time period to which the observation value applies. The specified instant is not considered part of the observation.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.
err.message === "COLLISION: ..."
The interval defined by start and end overlaps with one or more existing observations.

its.set(start, value, end)

Inserts an observation into the time series, overwriting any existing observations or portions of observations that would overlap. Returns a reference to its.

Arguments:

start (Date) indicates the start of the time period to which the observation value applies. The specified instant is considered part of the observation.
value (any type) is the value of the observation. Both null and undefined are valid observation values.
end (Date) indicates the end of the time period to which the observation value applies. The specified instant is not considered part of the observation.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.

its.clear(start, end)

Removes observations and portions of observations between start and end. Returns a reference to its.

Returns success whether or not the period contains any observations.

Arguments:

start (Date) is the start of the time period over which observations will be cleared. If an observation spans start then it will be truncated and the specified instant will be no longer be considered part of that observation.
end (Date) is the end of the time period over which observations will be cleared. If an observation spans end then it will be truncated and the specified instant will continue to be considered part of that observation.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.

its.split(date)

Breaks an observation into two observations at date, each with the same value as the original observation. Returns a reference to its.

If the observation at date begins at exactly date then no changes are made to the time series.

Arguments:

date (Date) is the date at which to split the observation. After the split, the referenced instant will be considered part of the later observation.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more of the supplied arguments.
err.message === "MISSING: ..."
There was no observation at date.

its.count()

Returns a number indicating the number of observations in the series.

Errors thrown: None

its.first()

Returns an IrregularPeriod object referencing the start of the earliest observation in the series.

Errors thrown:

err.message === "MISSING: ..."
The time series has no observations.

its.last()

Returns an IrregularPeriod object referencing the start of the latest observation in the series.

Errors thrown:

err.message === "MISSING: ..."
The time series has no observations.

its.each(fn)

Iterates chronologically over each observation in the time series calling fn(ip), where ip is an IrregularPeriod object referencing the start of the observation. Returns a reference to its.

Iteration is performed using ip.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

its.eachPeriod(fn)

Iterates chronologically from the first to the last observation in the time series calling fn(ip) on all intervening periods, including observations and gaps between observations. Each ip argument is an IrregularPeriod object referencing the start of the current period. Returns a reference to its.

Iteration is performed using ip.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each period as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

its.map(fn)

Returns a new IrregularSeries object constructed by iterating chronologically through all observations in its, calling fn(v) for each observation, where v is the observation value, and assigning the return value as the observation value for the same period in the new time series.

Iteration is performed using ip.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

its.reduce(fn)

Iterates chronologically over each observation in its calling fn(accumulator, ip), where accumulator is the current accumulator value and ip is an IrregularPeriod object referencing the start of the current observation.

The value returned by fn for the final observation is returned as the overall method result. The return type is therefore determined by the application.

If there are no observations in its then initialAccumulator is returned.

Iteration is performed using ip.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.
initialAccumulator (any type) is passed as an argument with the first call to fn.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

its.filter(fn)

Returns a new IrregularSeries created by iterating chronologically over each observation in its and calling fn(ip), where ip is an IrregularPeriod referencing the start of the observation. If fn returns truthy, the observation is included in the new series, otherwise it is omitted.

Iteration is performed using ip.obs.forward() after each call to fn, so time series modifications by fn are reflected.

Arguments:

fn (Function) is called once for each observation as described above. It is run in the global scope.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.
Errors thrown by fn are cascaded.

its.subSeries(start, end)

Returns a new IrregularSeries containing observations and portions of observations between start and end.

Arguments:

start (Date) indicates the starting date for the sub series.
end (Date) indicates the ending date for the sub series.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

its.overlay(its2)

Returns a new IrregularSeries object with observations from both its and its2. When observations in its and its2 would overlap, the observation in its2 takes precedence and the observation(s) in its are truncated or discarded as required.

Arguments:

its2 (IrregularSeries) is the time series to be overlaid on its.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
One or more of the supplied arguments was invalid.

its.clone()

Returns a new IrregularSeries with the same observations as its.

Errors thrown: None

its.serialize()

Returns a string JSON-TimeSeries serialization of the time series. Dates in the serialization are expressed in UTC.

Observation values must be JSON-serializable using JSON.stringify().

Errors thrown:

err.message === "NOT_SERIALIZABLE: ..."
The time series could not be serialized due to the presence of an undefined value, a NaN value, or a cyclical structure in one or more observations.

its.reset()

Clears all observations and returns a reference to its.

Errors thrown: None

its.type()

Returns "irregular".

Errors thrown: None

IrregularPeriod Objects

Time periods in an irregular time series are represented using IrregularPeriod objects, which allow applications to navigate and modify observations.

Each IrregularPeriod object references a specific instant in an irregular time series. Depending on the time series and reference date, an IrregularPeriod object may reference an observation, a gap between observations, the time before the first observation, or the time after the last observation.

Observations are stored in the IrregularSeries object, so modifications made to a time series through one IrregularPeriod object are immediately reflected in all other IrregularPeriod objects associated with that time series.

Methods

ip.series()

Returns a reference to the associated IrregularSeries object.

Errors thrown: None

ip.date()

Returns a Date indicating the instant referenced by ip.

ip.start()

Returns a Date indicating the start of the current period.

If an observation spans ip.date(), then the start of the observation is returned. The referenced instant is considered part of the observation.

If ip.date() references a gap between observations, then the end of the previous observation is returned. The referenced instant is considered part of the gap between observations, not part of the previous observation.

If ip.date() is before the first observation, then null is returned.

Errors thrown: None

ip.end()

Returns a Date indicating the end of the current period.

If an observation spans ip.date(), then the end of the observation is returned. The referenced instant is not considered part of the observation.

If ip.date() references a gap between observations, then the start of the next observation is returned. The referenced instant is considered part of the next observation, not part of the gap between observations.

If ip.date() is after the last observation, then null is returned.

Errors thrown: None

ip.hasForward()

Returns true unless (1) ip references the last observation in the series, (2) ip references an instant after the last observation in the series, or (3) the time series has no observations.

If ip.hasForward() returns true then it is valid to call ip.forward().

Errors thrown: None

ip.forward()

Returns a new IrregularPeriod object referencing the next period in the series, which could be an observation or a gap between observations.

The start of the next period serves as the reference date for the returned IrregularPeriod object.

Errors thrown:

err.message === "MISSING: ..."
The ip object already references the last observation in the time series, an instant after the last observation in the series, or the time series contains no observations.

ip.hasBack()

Returns true unless (1) ip references the first observation in the series, (2) ip references an instant before the first observation in the series, or (3) the time series has no observations.

If ip.hasBack() returns true then it is valid to call ip.back().

Errors thrown: None

ip.back()

Returns a new IrregularPeriod object referencing the previous period in the series, which could be an observation or a gap between observations.

The start of the previous period serves as the reference date for the returned IrregularPeriod object.

Errors thrown:

err.message === "MISSING: ..."
The ip object already references the first observation in the time series, an instant before the first observation in the series, or the time series contains no observations.

ip.obs.exists()

Returns true if the period referenced by ip contains an observation and false otherwise.

Returns false if there are no observations, or if ip references a date before the first observation or after the last observation.

Errors thrown: None

ip.obs.set(value)

Sets the observation value for the period referenced by ip and returns a reference to ip.

Arguments:

value (any type) is the value of the observation. Both null and undefined are valid observation values.

Errors thrown:

err.message === "INVALID_ARGUMENT: ..."
There was a problem with one or more supplied arguments.
err.message === "INVALID_PERIOD: ..."
The date referenced by ip is before the first observation or after the last observation in the series, or the series has no observations. Use its.add(...) instead.

ip.obs.clear()

Clears the observation value for the period referenced by ip and returns a reference to ip.

Returns success if there is no observation for the period.

Errors thrown:

err.message === "INVALID_PERIOD: ..."
The date referenced by ip is before the first observation or after the last observation in the series, or the series has no observations.

ip.obs.value()

Returns the observation value for the period.

Errors thrown:

err.message === "MISSING: ..."
There is no observation assigned to the period.
err.message === "INVALID_PERIOD: ..."
The date referenced by ip is before the first observation or after the last observation in the series, or the series has no observations.

ip.obs.hasForward()

Returns true unless (1) ip references the last observation in the series, (2) ip references an instant after the last observation in the series, or (3) the time series has no observations.

If ip.obs.hasForward() returns true then it is valid to call ip.obs.forward().

Errors thrown: None

ip.obs.forward()

Returns a new IrregularPeriod object referencing the next observation in the time series.

The start of the next observation serves as the reference date for the returned IrregularPeriod object.

Errors thrown:

err.message === "MISSING: ..."
There are no observations beginning after its.date().

ip.obs.hasBack()

Returns true unless (1) ip references the first observation in the series, (2) ip references an instant before the first observation in the series, or (3) the time series has no observations.

If ip.obs.hasBack() returns true then it is valid to call ip.obs.back().

Errors thrown: None

ip.obs.back()

Returns a new IrregularPeriod object referencing the previous observation in the time series.

The start of the previous observation serves as the reference date for the returned IrregularPeriod object.

Errors thrown:

err.message === "MISSING: ..."
There are no observations ending before or on its.date().

Examples

Initialize, populate, and display an irregular time series:

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  "value 1",
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  "value 2",
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  "value 3",
  new Date("2004-01-01T00:00:00.000Z")
);

// Display
its.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

Increment all observation values by 10 using its.map():

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  1,
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  2,
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  3,
  new Date("2004-01-01T00:00:00.000Z")
);

// Display original
its.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

// Generate an incremented series
var its2 = its.map(function (val) { return val + 10; });

// Display new
its2.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

Sum observation values using its.reduce():

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  1,
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  2,
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  3,
  new Date("2004-01-01T00:00:00.000Z")
);

// Sum and display
var sum = its.reduce(function (accum, ip) {
  return accum + ip.obs.value();
}, 0);
console.log(sum);

Retrieve observations with negative values using its.filter():

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  -1,
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  0,
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  1,
  new Date("2004-01-01T00:00:00.000Z")
);

// Display original
its.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

// Generate a filtered series
var its2 = its.filter(function (ip) {
  return ip.obs.value() < 0;
});

// Display new
its2.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

Serialize and unserialize an irregular time series:

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  -1,
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  0,
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  1,
  new Date("2004-01-01T00:00:00.000Z")
);

// Display original
its.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

// Serialize and display
var ser = its.serialize();
console.log(ser);

// Unserialize and display
var its2 = chronology.unserialize(ser);
its2.each(function(ip) {
  console.log(ip.start(), ip.end(), ip.obs.value());
});

Unserializing a Time Series

Regular and irregular time series represented in JSON-TimeSeries format can be unserialized as follows:

var ts = chronology.unserialize(jsonTs, subPeriodBoundaries);

The resulting value of ts is either a RegularSeries or an IrregularSeries object. The type of series can be determined using ts.type().

Arguments:

jsonTs (required string) is a time series serialized in JSON-TimeSeries format.
subPeriodBoundaries (optional function) specifies how to determine sub period boundaries for regular time series. The option is disregarded for irregular time series. See the regular time series initialization documentation for details.

Errors thrown:

`err.message === "INVALID_ARGUMENT: ..."

One or more supplied arguments was invalid.

`err.message === "INVALID_JSONTS: ..."

There was a problem with the structure of jsonTs.

`err.message === "NOT_SUPPORTED: ..."

The supplied time series uses a JSON-TimeSeries feature that is not supported by this library.

Extending the Library

Extending RegularSeries

The prototype for RegularSeries objects is exposed as chronology.regular.proto for extension.

For example, a sum() function can be added as follows:

chronology.regular.proto.sum = function() {
  return this.reduce(function(a, rp) {
    return a + rp.obs.value();
  }, 0);
};

And used as follows:

// Initialize
var rts = chronology.regular({
  basePeriod: [1, "y"]
});

// Populate
var period = rts.period(new Date("2000-01-01"));
for (var i = 0; i < 10; i += 1) {
  period.obs.set(i);
  period = period.forward();
}

// Use the sum function
console.log(rts.sum());

Extending IrregularSeries

The prototype for IrregularSeries objects is exposed as chronology.irregular.proto for extension.

For example, a sum() function can be added as follows:

chronology.irregular.proto.sum = function() {
  return this.reduce(function(a, ip) {
    return a + ip.obs.value();
  }, 0);
};

And used as follows:

// Initialize
var its = chronology.irregular();

// Populate
its.add(
  new Date("2000-01-01T00:00:00.000Z"),
  1,
  new Date("2001-02-03T12:34:56.789Z")
);
its.add(
  new Date("2003-04-05T00:12:34.567Z"),
  2,
  new Date("2003-04-05T12:45:56.789Z")
);
its.add(
  new Date("2003-04-05T12:45:56.789Z"),
  3,
  new Date("2004-01-01T00:00:00.000Z")
);

// Use the sum function
console.log(its.sum());

Published

Vulnerabilities

Links

Maintainers

Keywords

Readme

Chronology.js

Getting Started

Installing the Package

NPM

CDN

JSON-TimeSeries Primer

Regular Time Series API

RegularSeries Objects

Initialization

Methods

rts.period(date)

rts.period(basePeriodDate, subPeriod)

rts.count()

rts.first()

rts.last()

rts.each(fn)

rts.eachPeriod(fn)

rts.map(fn)

rts.transform(fn)

rts.reduce(fn, initialAccumulator)

rts.filter(fn)

rts.subSeries(rpStart, rpEnd)

rts.overlay(rts2)

rts.clone()

rts.serialize()

rts.reset()

rts.type()

rts.anchor()

rts.subPeriods()

RegularPeriod Objects

Methods

rp.series()

rp.start()

rp.end()

rp.basePeriodStart()

rp.basePeriodEnd()

rp.subPeriod()

rp.index()

rp.forward(num)

rp.back(num)

rp.obs.exists()

rp.obs.set(value)

rp.obs.clear()

rp.obs.value()

rp.obs.hasForward()

rp.obs.forward()

rp.obs.hasBack()

rp.obs.back()

Examples

Irregular Time Series API

IrregularSeries Objects

Initialization

Methods

its.period(date)

its.add(start, value, end)

its.set(start, value, end)

its.clear(start, end)

its.split(date)

its.count()

its.first()

its.last()

its.each(fn)

its.eachPeriod(fn)

its.map(fn)

its.reduce(fn)

its.filter(fn)

its.subSeries(start, end)

its.overlay(its2)

its.clone()

its.serialize()

its.reset()

its.type()

IrregularPeriod Objects

Methods