{"id":13571094,"url":"https://github.com/TimeSynth/TimeSynth","last_synced_at":"2025-04-04T07:32:57.682Z","repository":{"id":41583613,"uuid":"62732998","full_name":"TimeSynth/TimeSynth","owner":"TimeSynth","description":"A Multipurpose Library for Synthetic Time Series Generation in Python","archived":false,"fork":false,"pushed_at":"2023-10-26T22:59:57.000Z","size":3013,"stargazers_count":326,"open_issues_count":12,"forks_count":62,"subscribers_count":19,"default_branch":"master","last_synced_at":"2024-04-24T16:04:20.768Z","etag":null,"topics":["generator","python","python3","series","time-series","timeseries"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TimeSynth.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-07-06T15:40:50.000Z","updated_at":"2024-06-07T22:54:55.143Z","dependencies_parsed_at":"2024-06-07T23:05:01.132Z","dependency_job_id":null,"html_url":"https://github.com/TimeSynth/TimeSynth","commit_stats":{"total_commits":99,"total_committers":7,"mean_commits":"14.142857142857142","dds":0.4242424242424242,"last_synced_commit":"e50cdb9015d415adf46a4eae161a087c5c378564"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimeSynth%2FTimeSynth","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimeSynth%2FTimeSynth/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimeSynth%2FTimeSynth/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimeSynth%2FTimeSynth/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TimeSynth","download_url":"https://codeload.github.com/TimeSynth/TimeSynth/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247139532,"owners_count":20890250,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["generator","python","python3","series","time-series","timeseries"],"created_at":"2024-08-01T14:00:58.394Z","updated_at":"2025-04-04T07:32:52.674Z","avatar_url":"https://github.com/TimeSynth.png","language":"Jupyter Notebook","readme":"[![Build Status](https://travis-ci.org/TimeSynth/TimeSynth.svg?branch=master)](https://travis-ci.org/TimeSynth/TimeSynth) [![codecov](https://codecov.io/gh/TimeSynth/TimeSynth/branch/master/graph/badge.svg)](https://codecov.io/gh/TimeSynth/TimeSynth)\n\n# TimeSynth\n_Multipurpose Library for Synthetic Time Series_\n\n**Please cite as:**   \nJ. R. Maat, A. Malali, and P. Protopapas, “TimeSynth: A Multipurpose Library for Synthetic Time Series in Python,” 2017. [Online]. Available: http://github.com/TimeSynth/TimeSynth\n\n**TimeSynth** is an open source library for generating synthetic time series for\nmodel testing. The library can generate regular and irregular time series. The architecture\nallows the user to match different signals with different architectures allowing\na vast array of signals to be generated. The available signals and noise types are\nlisted below.\n\nN.B. We only support Python 3.6+ at this time.\n\n#### Signal Types\n* Harmonic functions(sin, cos or custom functions)\n* Gaussian processes with different kernels\n    * Constant\n    * Squared exponential\n    * Exponential\n    * Rational quadratic\n    * Linear\n    * Matern\n    * Periodic\n* Pseudoperiodic signals\n* Autoregressive(p) process\n* Continuous autoregressive process (CAR)\n* Nonlinear Autoregressive Moving Average model (NARMA)\n\n#### Noise Types\n* White noise\n* Red noise\n\n### Installation\nTo install the package via github,\n```{bash}\ngit clone https://github.com/TimeSynth/TimeSynth.git\ncd TimeSynth\npython setup.py install\n```\n\n### Using TimeSynth\n```shell\n$ python\n```\nThe code snippet demonstrates creating a irregular sinusoidal signal with white noise.\n```python\n\u003e\u003e\u003e import timesynth as ts\n\u003e\u003e\u003e # Initializing TimeSampler\n\u003e\u003e\u003e time_sampler = ts.TimeSampler(stop_time=20)\n\u003e\u003e\u003e # Sampling irregular time samples\n\u003e\u003e\u003e irregular_time_samples = time_sampler.sample_irregular_time(num_points=500, keep_percentage=50)\n\u003e\u003e\u003e # Initializing Sinusoidal signal\n\u003e\u003e\u003e sinusoid = ts.signals.Sinusoidal(frequency=0.25)\n\u003e\u003e\u003e # Initializing Gaussian noise\n\u003e\u003e\u003e white_noise = ts.noise.GaussianNoise(std=0.3)\n\u003e\u003e\u003e # Initializing TimeSeries class with the signal and noise objects\n\u003e\u003e\u003e timeseries = ts.TimeSeries(sinusoid, noise_generator=white_noise)\n\u003e\u003e\u003e # Sampling using the irregular time samples\n\u003e\u003e\u003e samples, signals, errors = timeseries.sample(irregular_time_samples)\n```\n","funding_links":[],"categories":["📦 Packages","Jupyter Notebook"],"sub_categories":["Python"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTimeSynth%2FTimeSynth","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTimeSynth%2FTimeSynth","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTimeSynth%2FTimeSynth/lists"}