{"id":21373566,"url":"https://github.com/kirlf/lte-kpi-ts-forecasting","last_synced_at":"2025-07-13T07:33:05.120Z","repository":{"id":38080118,"uuid":"497914259","full_name":"kirlf/lte-kpi-ts-forecasting","owner":"kirlf","description":"INVESTIGATION OF THE BAYESIAN AND NON-BAYESIAN TIME SERIES FORECASTING FRAMEWORKS IN APPLICATION TO OSS SYSTEMS OF THE LTE/LTE-A AND 5G MOBILE NETWORKS","archived":false,"fork":false,"pushed_at":"2022-06-10T18:31:54.000Z","size":1805,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-03-05T14:24:07.288Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/kirlf.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}},"created_at":"2022-05-30T11:30:59.000Z","updated_at":"2022-06-03T18:53:53.000Z","dependencies_parsed_at":"2022-08-31T02:11:01.678Z","dependency_job_id":null,"html_url":"https://github.com/kirlf/lte-kpi-ts-forecasting","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kirlf%2Flte-kpi-ts-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kirlf%2Flte-kpi-ts-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kirlf%2Flte-kpi-ts-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kirlf%2Flte-kpi-ts-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kirlf","download_url":"https://codeload.github.com/kirlf/lte-kpi-ts-forecasting/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225867939,"owners_count":17536930,"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":[],"created_at":"2024-11-22T08:29:15.535Z","updated_at":"2024-11-22T08:29:16.204Z","avatar_url":"https://github.com/kirlf.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kirlf/lte-kpi-ts-forecasting/blob/main/src/E_RAB_SETUP_FR.ipynb)\n\nThis repository contains data and source code of the research that was puplished in the following article:\n\n\u003e Fadeev V.A., Zaidullin S.V., Nadeev A.F. (2022). [Investigation of the Bayesian and non-Bayesian time series forecasting framewo rks in appli-cation to OSS systems of the LTE/LTE-A and 5G mobile networks.](https://www.researchgate.net/publication/360919297_INVESTIGATION_OF_THE_BAYESIAN_AND_NON-BAYESIAN_TIME_SERIES_FORECASTING_FRAMEWORKS_IN_APPLICATION_TO_OSS_SYSTEMS_OF_THE_LTELTE-A_AND_5G_MOBILE_NETWORKS) T-Comm, vol. 16, no.4, pр. 52-60. \n\nFor better visualization use the following link:\n\n\u003e [E_RAB_SETUP_FR.ipynb](https://nbviewer.org/github/kirlf/lte-kpi-ts-forecasting/blob/main/src/E_RAB_SETUP_FR.ipynb) (NBViewer - Jupyter Notebook)\n\nIf local usage is preferable but `Jupyter` is not locally installed, use the `docker-compose` to run application (some problems may occur in MacOS, unfortunately).\n\n## Next steps\n\nI guess, the following material:\n\u003e [Predictive Analytics: Time-Series Forecasting with GRU and BiLSTM in TensorFlow](https://towardsdatascience.com/predictive-analytics-time-series-forecasting-with-gru-and-bilstm-in-tensorflow-87588c852915)\n\ncan be uses as an example for the next student research projects.\n\nM.Sc. Vladimir Fadeev\nKazan, 2022\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkirlf%2Flte-kpi-ts-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkirlf%2Flte-kpi-ts-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkirlf%2Flte-kpi-ts-forecasting/lists"}