1. User Guide

1.1. QuickStart

1.1.1. TimeSeries

import timeseries
from timeseries import TimeSeries
from pylab import random
noise = random(1024)
ts = TimeSeries(noise)
ts.mean
ts.plot()

[hires.png, pdf]

1.1.2. Lag Plot and data sets

There are some data sets available in the module data. From one of these data sets, we can look at available plots.

from timeseries import data
d = data.get_nottem_data()
d.plot()

from timeseries import lagplot
lagplot(d, nlag=4, nrow=2, ncol=2)

[hires.png, pdf]

1.1.3. Financial Data

from timeseries import finance
from finance import FinancialData
from datetime import datetime

d1 = datetime(2000,1,1)
d2 = datetime(2010,1,1)

# obtain arcelor mittal data from d1 to d2
fd = FinancialData('MT.PA', d1, d2)

# get the volumes
fd.data.volume

# plot some summary data
fd.plot_summary()

[hires.png, pdf]