이 문서에서는 BigQuery ML의 내장 TimesFM 시계열 예측 모델에 대해 설명합니다.
내장된 TimesFM 단변량 모델은 Google 연구팀의 오픈소스 TimesFM 모델을 구현한 것입니다. Google Research의 TimesFM 모델은 시계열 예측을 위한 파운데이션 모델로, 수많은 실제 데이터 세트에서 수십억 개의 시간 포인트를 사전 학습했기 때문에 다양한 도메인의 새로운 예측 데이터 세트에도 적용할 수 있습니다.
TimesFM 모델은 BigQuery에서 지원되는 모든 리전에서 사용할 수 있습니다.
AI.FORECAST 함수와 함께 BigQuery ML의 내장 TimesFM 모델을 사용하면 자체 모델을 만들고 학습시키지 않고도 예측을 실행할 수 있으므로 모델 관리가 필요하지 않습니다.
TimesFM 모델의 예측 결과는 ARIMA와 같은 기존 통계 방법과 비교할 수 있습니다. TimesFM 모델에서 제공하는 것보다 더 많은 모델 조정 옵션을 원하는 경우 ARIMA_PLUS 또는 ARIMA_PLUS_XREG 모델을 만들어 대신 ML.FORECAST 함수와 함께 사용할 수 있습니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[],[],null,["# The TimesFM model\n=================\n\n|\n| **Preview**\n|\n|\n| This product or feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA products and features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n| **Note:** To give feedback or request support for this feature, contact [bqml-feedback@google.com](mailto:bqml-feedback@google.com).\n\nThis document describes BigQuery ML's built-in\nTimesFM time series forecasting model.\n\nThe built-in TimesFM univariate model is an implementation of Google Research's\nopen source\n[TimesFM model](https://github.com/google-research/timesfm). The Google Research\nTimesFM model is a foundation model for time-series forecasting that has been\npre-trained on billions of time-points from many real-world datasets, so you\ncan apply it to new forecasting datasets across many domains.\nThe TimesFM model is available in all BigQuery supported regions.\n\nUsing BigQuery ML's built-in TimesFM model with the\n[`AI.FORECAST` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast)\nlets you perform\nforecasting without having to create and train your own model, so you can\navoid the need for model management.\nThe forecast results from the TimesFM model are comparable to\nconventional statistical methods such as ARIMA. If you want more\nmodel tuning options than the TimesFM model offers, you can create an\n[`ARIMA_PLUS`](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series)\nor\n[`ARIMA_PLUS_XREG`](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-multivariate-time-series)\nmodel and use it with the\n[`ML.FORECAST` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-forecast)\ninstead.\n\nTo try using a TimesFM model with the `AI.FORECAST` function, see\n[Forecast multiple time series with a TimesFM univariate model](/bigquery/docs/timesfm-time-series-forecasting-tutorial).\n\nTo learn more about the Google Research TimesFM model, use the following\nresources:\n\n- [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/)\n- [GitHub repository](https://github.com/google-research/timesfm)\n- [Hugging Face page](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6)"]]
