Installation

Just pip-install it:

$ pip install tshistory_refinery

Create first a postgresql database:

$ createdb my_time_series

Then initialize the database schema:

$ tsh init-db postgresql:///my_time_series --no-dry-run

Last item: we need a configuration file refinery.cfg in the home directory, containing:

[db]
uri = postgresql:///my_time_series

Introduction

Purpose

tshistory is targetted at applications using time series where backtesting and cross-validation are an essential feature.

It provides exhaustivity and efficiency of the storage, with a simple Python api.

It can be used as a building block for machine learning, model optimization and validation, both for inputs and outputs.

Principles

There are many ways to represent timeseries in a relational database, and tshistory provides two things:

• a base python API which abstracts away the underlying storage

• a postgres model, which emphasizes the compact storage of successive states of series

The core idea of tshistory is to handle successive versions of timeseries as they grow in time, allowing to get older states of any series.

Timeseries Store Usage

Starting with a fresh database

You need a postgresql database. You can create one like this:

createdb mydb

Then, initialize the tshistory tables, like this:

tsh init-db postgresql://me:password@localhost/mydb

From this you’re ready to go !

Creating a series

However here’s a simple example:

>>> import pandas as pd
>>> from tshistory.api import timeseries
>>>
>>> tsa = timeseries('postgres://me:password@localhost/mydb')
>>>
>>> series = pd.Series([1, 2, 3],
...                    pd.date_range(start=pd.Timestamp(2017, 1, 1),
...                                  freq='D', periods=3))
# db insertion
>>> tsa.update('my_series', series, 'babar@pythonian.fr')
...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    3.0
Freq: D, Name: my_series, dtype: float64

# note how our integers got turned into floats
# (there are no provisions to handle integer series as of today)

# retrieval
>>> tsa.get('my_series')
...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    3.0
Name: my_series, dtype: float64

Note that we generally adopt the convention to name the time series api object tsa.

Updating a series

This is good. Now, let’s insert more:

 >>> series = pd.Series([2, 7, 8, 9],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 2),
 ...                                  freq='D', periods=4))
 # db insertion
 >>> tsa.update('my_series', series, 'babar@pythonian.fr')
 ...
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64

 # you get back the *new information* you put inside
 # and this is why the `2` doesn't appear (it was already put
 # there in the first step)

 # db retrieval
 >>> tsa.get('my_series')
 ...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64

It is important to note that the third value was replaced, and the two last values were just appended. As noted the point at 2017-1-2 wasn’t a new information so it was just ignored.

Retrieving history

We can access the whole history (or parts of it) in one call:

>>> history = tsa.history('my_series')
...
>>>
>>> for idate, series in history.items(): # it's a dict
...     print('insertion date:', idate)
...     print(series)
...
insertion date: 2018-09-26 17:10:36.988920+02:00
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    3.0
Name: my_series, dtype: float64
insertion date: 2018-09-26 17:12:54.508252+02:00
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64

Note how this shows the full serie state for each insertion date. Also the insertion date is timzeone aware.

Specific versions of a series can be retrieved individually using the get method as follows:

>>> tsa.get('my_series', revision_date=pd.Timestamp('2018-09-26 17:11+02:00'))
...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    3.0
Name: my_series, dtype: float64
>>>
>>> tsa.get('my_series', revision_date=pd.Timestamp('2018-09-26 17:14+02:00'))
...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64

It is possible to retrieve only the differences between successive insertions:

>>> diffs = tsa.history('my_series', diffmode=True)
...
>>> for idate, series in diffs.items():
...   print('insertion date:', idate)
...   print(series)
...
insertion date: 2018-09-26 17:10:36.988920+02:00
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    3.0
Name: my_series, dtype: float64
insertion date: 2018-09-26 17:12:54.508252+02:00
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64

You can see a series metadata:

>>> tsa.update_metadata('series', {'foo': 42})
>>> tsa.metadata('series')
{foo: 42}