Make Quarterly Time Series in Julia - julia

Question

In Julia, we can create a Time Array with the following code:
d = [date(1980,1,1):date(2015,1,1)];
t = TimeArray(d,rand(length(d)),["test"])
This would give us daily data. What about getting quarterly or yearly time series?

Answer 1

Merely use the optional step capability of Base.range in combination with the type Datetime.Period
julia> [Date(1980,1,1):Month(3):Date(2015,1,1)]
141-element Array{Date{ISOCalendar},1}:
1980-01-01
1980-04-01
1980-07-01
1980-10-01
1981-01-01
1981-04-01
...
And change the step as necessary
julia> [Date(1980,1,1):Year(1):Date(2015,1,1)]
36-element Array{Date{ISOCalendar},1}:
1980-01-01
1981-01-01
1982-01-01
...
0.3.x vs 0.4.x
In version 0.3.x Dates is available in the package Dates which provides the module Dates, but in version 0.4.x, module Dates is built in. Also (currently) an additional subtle difference is Year and Month must be accessed as Dates.Year and Dates.Month in version 0.4.x.

Answer 2

I know this question is a bit old, but it's worth adding that there is another time series package called Temporal* that has this functionality available.
Here's some example usage:
using Temporal, Base.Dates
date_array = collect(today()-Day(365):Day(1):today())
random_walk = cumsum(randn(length(date_array))) + 100.0
Construct the time series object (type TS). Last argument is for column names, but if not given will autogenerate default column names.
ts_data = TS(random_walk, date_array, :RandomWalk)
# Index RandomWalk
# 2016-08-24 99.8769
# 2016-08-25 99.1643
# 2016-08-26 98.8918
# 2016-08-27 97.7265
# 2016-08-28 97.9675
# 2016-08-29 97.7151
# 2016-08-30 97.0279
# ⋮
# 2017-08-17 81.2998
# 2017-08-18 82.0658
# 2017-08-19 82.1941
# 2017-08-20 81.9021
# 2017-08-21 81.8163
# 2017-08-22 81.5406
# 2017-08-23 81.2229
# 2017-08-24 79.2867
Get the last observation of every quarter (similar logic exists for weeks, months, and years using eow, eom, and eoy respectively):
eoq(ts_data) # get the last observation at every quarter
# 4x1 Temporal.TS{Float64,Date}: 2016-09-30 to 2017-06-30
# Index RandomWalk
# 2016-09-30 88.5629
# 2016-12-31 82.1014
# 2017-03-31 84.9065
# 2017-06-30 92.1997
Can also use functions to aggregate data by the same kinds of periods as given above.
collapse(ts_data, eoq, fun=mean) # get the average value every quarter
# 4x1 Temporal.TS{Float64,Date}: 2016-09-30 to 2017-06-30
# Index RandomWalk
# 2016-09-30 92.5282
# 2016-12-31 86.8291
# 2017-03-31 89.1391
# 2017-06-30 90.3982
* (Disclaimer: I'm the package author.)

Answer 3

Quarterly isn't supported yet, but other time periods such as week, month and year are supported. There is a method called collapse that is used to convert a TimeArray to a larger time frame.
d = [Date(1980,1,1):Date(2015,1,1)];
t = TimeArray(d,rand(length(d)),["test"])
c = collapse(t, last, period=year)
Returns the following
36x1 TimeArray{Float64,1} 1980-12-31 to 2015-01-01
test
1980-12-31 | 0.94
1981-12-31 | 0.37
1982-12-31 | 0.12
1983-12-31 | 0.64
⋮
2012-12-31 | 0.43
2013-12-31 | 0.81
2014-12-31 | 0.88
2015-01-01 | 0.55
Also, note that date has been deprecated in favor of Date as a new updated package now runs the date/time functions underneath.