1. Quick Start

1.1. Install khiva

First of all, the KHIVA C++ library should be installed by using the installer that we offer in http://www.shapelets.io

Then, the compiled KHIVA package is hosted on the Python Package Index (PyPI) so it can be installed with pip:

pip3 install khiva

1.2. Dive in

In order to quickly dive into khiva, you can follow the following example:

First step, consists in setting what backend and device you want to use (there is a backend and a device set by default):

from khiva.library import *

After that, we can create an array in the device:

from khiva.array import *
a = Array([1, 2, 3, 4, 5, 6, 7, 8])

The lines contained above print the dimensions and the content of the created array:

[8 1 1 1]

We have to know that this array is created on the device and now we can concatenate operations applied to this array in an asynchronous way and we will only receive the data in the host when ‘to_list()’, ‘to_numpy()’ or ‘to_pandas()’ (this one only supports bi-dimensional time series) functions are called.

a = a.to_pandas()

The result is the next one:

1 1.0
2 2.00
3 3.00
4 4.00
5 5.00
6 6.00
7 7.00
8 8.00

Now let’s dive into the asynchronous usage of the library.

KHIVA’s library provide us several time series analysis functionalities which include features extaction, time-series re-dimension, distance calculations, motifs and discords detection, tools for similarity study, statistical parameters extraction or time series normalization.

All of these functionalities can be concatenated in order to improve the performance, getting the data just in the moment that you will not use functions of this library:

from khiva.matrix import *
stomp_result = stomp(Array(np.array([11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11])),
                         Array(np.array([9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9])),
find_best_n_discords_result = find_best_n_discords(stomp_result[0],
                                                       stomp_result[1], 2)
a = find_best_n_discords_result[2].to_numpy()

The previous produces the following output:

[1.73190141 1.73185158] [8 8] [0 9]

The first numpy array represents the minimum distances between the subsequences of length 3 between the two time series. The second numpy array represents the location of those subsequences in the first time series and the third one represents the indices in the second time series.

Another interesting thing that we want to demonstrate in this introductory section is the possibility of using the library for computing the functions in different backends and with different devices, knowing that the operations should be executed in the same device where the array was created.

#Adding operations in the different backends and devices.
from khiva.features import *
a = Array([1, 2, 3, 4, 5, 6, 7, 8])
b = mean(a)

c = Array([1, 2, 3, 4, 5, 6, 7, 8])
d = mean(c)

e = Array([1, 2, 3, 4, 5, 6, 7, 8])
f = mean(e)

#Retrieving the results of the previous operations



The output is the next one:


Another important fact is that, by default, the data type used is floating point of 32 bits in order to not have problems with the different devices, but it can be changed deliberately.

The available data types are the next ones:

Data type Explanation
f32 32 bits Float
c32 32 bits Complex
f64 64 bits Double
c64 64 bits Complex
b8 8 bits Boolean
s32 32 bits Int
32u 32 bits Unsigned Int
u8 8 bits Unsigned Int
s64 64 bits Integer
u64 64 bits Unsigned Int
s16 16 bits Int
u16 16 bits Unsigned Int

There are functions that do not support 32 bits floating point data type, so it is necessary to indicate the data type. The following is an example function requiring a 32bit signed integer array:

cwt_coefficients_result = cwt_coefficients(Array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]),
                                            Array(data=[1, 2, 3], khiva_type=dtype.s32), 2, 2).to_numpy()

The output is:

[0.26517162 0.26517162]

1.3. Limitations

This open-source library provides a very good performance but it has got memory limitations. For cases where you need to apply a time series analysis over a huge amount of data and in short-term fashion, please, contact us. We are working on a cluster version which is coming soon.

1.4. Let’s Rock!

Now, you have the basic concepts to start using the library. Please, follow the documentation of each function in order to know how to use them. Each function has its corresponding test(s). You can check there how to use the function.

Furthermore, we provide use cases and examples that you can use to learn where and how to apply the library.