Package: tim-client
TIM Python ClientTIM, or Tangent Information Modeler, is Tangent Works’ automatic model building engine. It is designed specifically for time-series forecasting and anomaly detection.The TIM Python client introduces an easy and fast way to use TIM in any Python project. As an abstraction overTIM's API, it encapsulates the logic into useful and performant functions helping users go from time-series data to insights that can generate business value.UsageInstallationTo install the package run:pip install tim-clientInitializationimporttimclient=tim.Tim(email='',password='')Error handlingMinimal validation is performed by the Tim client, errors will be raised by the server.DocumentationFull documentation of the API can be found at:https://docs.tangent.worksExamplesFor examples, code templates and case studies using the Python Client, please visit the Tangent Works GitHub page here:https://github.com/Tangent-WorksMethodsThe TIM Python client is a Python SDK to use the TIM Platform (v5) and is organized around the TIM API which can be found here:https://tim-platform.tangent.works/api/v5/swagger-ui.html#/Its exists in two levels. The first level consists of methods that call the API endpoints directly. This includes the following:client.user_groups.list_user_groupclient.user_groups.create_user_groupclient.user_groups.details_user_groupclient.user_groups.update_user_groupclient.user_groups.delete_user_groupclient.workspaces.list_workspaceclient.workspaces.create_workspaceclient.workspaces.details_workspaceclient.workspaces.edit_workspaceclient.workspaces.delete_workspaceclient.datasets.upload_datasetclient.datasets.update_datasetclient.datasets.list_datasetclient.datasets.delete_dataset_listclient.datasets.details_datasetclient.datasets.edit_dataset_detailsclient.datasets.delete_datasetclient.datasets.dataset_logsclient.datasets.logs_dataset_versionclient.datasets.list_dataset_versionsclient.datasets.details_dataset_versionclient.datasets.delete_dataset_versionclient.datasets.status_dataset_versionclient.datasets.slice_dataset_versionclient.use_cases.list_use_caseclient.use_cases.create_use_caseclient.use_cases.details_use_caseclient.use_cases.edit_use_caseclient.use_cases.delete_use_caseclient.experiments.list_experimentclient.experiments.create_experimentclient.experiments.details_experimentclient.experiments.edit_experimentclient.experiments.delete_experimentclient.forecasting.build_modelclient.forecasting.upload_modelclient.forecasting.rebuild_modelclient.forecasting.retrain_modelclient.forecasting.predictclient.forecasting.rcaclient.forecasting.what_ifclient.forecasting.job_listclient.forecasting.delete_job_listclient.forecasting.job_detailsclient.forecasting.delete_jobclient.forecasting.copy_jobclient.forecasting.executeclient.forecasting.job_logsclient.forecasting.statusclient.forecasting.status_collectclient.forecasting.results_tableclient.forecasting.results_production_forecastclient.forecasting.results_modelclient.forecasting.results_accuraciesclient.forecasting.results_rcaclient.forecasting.results_production_tableclient.forecasting.results_production_accuraciesclient.detection.build_kpi_modelclient.detection.build_system_modelclient.detection.build_outlier_modelclient.detection.build_drift_model_kolmogorov_smirnovclient.detection.build_drift_model_jensen_shannonclient.detection.upload_modelclient.detection.rebuild_kpi_modelclient.detection.detectclient.detection.rcaclient.detection.what_ifclient.detection.jobs_listclient.detection.delete_job_listclient.detection.job_detailsclient.detection.delete_jobclient.detection.copy_jobclient.detection.executeclient.detection.job_logsclient.detection.statusclient.detection.status_collectclient.detection.results_tableclient.detection.results_modelclient.detection.results_accuraciesclient.detection.results_rcaclient.detection.results_production_tableclient.detection.results_production_accuraciesclient.licenses.details_licenseclient.licenses.storage_licenseclient.users.details_userclient.telemetry.dataset_callsclient.telemetry.job_callsSecondly, higher level functions exist to facilitate the use of TIM and setting up MLOps pipelines through Python. This includes the following methods:client.poll_dataset_version_statusclient.poll_dataset_version_statusclient.upload_datasetclient.update_datasetclient.poll_forecast_statusclient.forecasting_job_resultsclient.execute_forecast_jobclient.forecasting_build_modelclient.forecasting_predictclient.forecasting_rebuild_modelclient.forecasting_retrain_modelclient.forecasting_results_rcaclient.forecasting_root_cause_analysisclient.forecasting_what_if_analysisclient.quick_forecastclient.poll_detect_statusclient.detection_job_resultsclient.execute_detection_jobclient.detection_build_kpi_modelclient.detection_build_system_modelclient.detection_build_outlier_modelclient.detection_build_drift_model_kolmogorov_smirnovclient.detection_build_drift_model_jensen_shannonclient.detection_rebuild_kpi_modelclient.detection_detectclient.detection_results_rcaclient.detection_root_cause_analysisclient.detection_what_if_analysisAbout Tangent WorksTangent Works delivers forecasting and anomaly detection capabilities for time series data in a fast, accurate and explainable way. This enables users to drive business value from predictive analytics, empowers them to take informed decisions and helps them improve processes.TIM has already been recognized as a winner in multiple competitions, including GEFCom 2017 and the 2017 ANDRITZ Hackathon.