https://github.com/nicholasjng/timeseries-thesis
Code for my thesis work in the field of Time Series Analysis, concerned with methods to detect regularity in time series.
https://github.com/nicholasjng/timeseries-thesis
Last synced: 2 months ago
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Code for my thesis work in the field of Time Series Analysis, concerned with methods to detect regularity in time series.
- Host: GitHub
- URL: https://github.com/nicholasjng/timeseries-thesis
- Owner: nicholasjng
- License: mit
- Created: 2020-08-26T07:50:50.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-12T13:44:49.000Z (about 4 years ago)
- Last Synced: 2025-01-20T18:11:50.588Z (4 months ago)
- Language: Python
- Size: 63.9 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: changepoint_detection/__init__.py
- License: LICENSE
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README
# General
This repository contains the code for my numerical experiments on the
gym data during my Master's Thesis.
## General Usage
This repository contains both convenience functions for operating on the data
as well as runnable scripts that compute different sorts of statistics
on the data. Before everything, it is advisable to create a new virtual
environment to avoid cluttering your system's native Python distribution.
### Pipenv
This repository contains a Pipfile for usage with Pipenv; for more information,
see the [Pipenv docs](https://pipenv-fork.readthedocs.io/en/latest/).
Installation on macOS is as easy as
```
brew install pipenv
```
provided you have Homebrew installed on your Mac. Afterwards, dependencies can
be installed from the Pipfile by simply running
```
pipenv install
```
in the top-level directory. The virtual environment can be activated by the
```
pipenv shell
```
command.
### Cython distribution
On top of the Python code, this repository also contains some functions written
in Cython to speed up computations on the data. Read about Cython usage
[here](https://cython.readthedocs.io/en/latest/). The compilation and
installation process is handled by Python's `setuptools` package. For
installation, from the top level directory complete these steps while inside
your virtual environment:
```
cd regularity_analysis
python setup.py build_ext --inplace
```
## Running statistics experiments
Some experiments on the dataset are located inside the `bin` folder. These are
based on a config file architecture: Command line arguments for statistical
functions and interesting hyperparameters for these functions can be specified
inside these yaml files. Upon running these scripts, the yaml files are then
envoked and parsed by the PyYaml module. Some sample configuration files are
located inside the `cfg` directory, though you can pass your own to the scripts
by supplying the --config_file flag.
Running a script takes one line. From the root of the repository, run
```
python -m bin.
```
*Notice that the file is run as a module, so no .py should be added at the end*.
For example, for running the `changepoint_detection` script file inside bin,
you should type
```
python -m bin.changepoint_detection
```
from the root directory. If you are working on the repository yourself, a
local installation could make this process easier, I did not figure out in time
how to do that.
### Other useful information
The code leverages mostly pandas and numpy/scipy, as the dataset is small
enough to fit into memory. Some data preparation code can be found inside the
Jupyter Notebooks, for those, just fire up a Jupyter Lab instance.