Getting Started

Installation

Using pip

TimeCaVe can be directly installed from PyPi using pip:

pip install timecave

To install the development version, simply type:

pip install "timecave[dev]"

This will install dependencies that have been used to develop TimeCaVe and its documentation, such as Black and MKDocs.

Using git

TimeCaVe can also be installed using git. To do so, clone the repository:

git clone https://github.com/MiguelLoureiro98/timecave.git

Then, move into the cloned repository and install the package using pip:

cd timecave
pip install .

Again, to install development dependencies, simply type:

pip install ".[dev]"

Basic Usage

TimeCaVe is, above all else, built to provide easy-to-use validation methods for time series forecasting models. The syntax is relatively similar to that of the methods provided by Scikit-learn (e.g. K-fold). Here is an example of how to use one of the methods provided by this package (Block Cross-Validation):

import numpy as np
from timecave.validation_methods.CV import BlockCV

ts = np.arange(0, 10)

# Split the data into 5 folds
splitter = BlockCV(5, ts);

for train, test, _ in splitter.split():

    training_data = ts[train];
    validation_data = ts[test];

    # Train and validate your model

For more information on how to use the package, please refer to our API reference, where detailed descriptions of every function and class are provided.