BigQuery ML 문의 기본 설정과 함수를 사용하면 ML 지식이 많지 않더라도 예측 모델을 만들고 사용할 수 있습니다. 하지만 ML 개발, 특히 예측 모델에 관한 기본 지식을 갖추면 데이터와 모델을 모두 최적화하여 더 나은 결과를 얻을 수 있습니다. 다음 리소스를 사용하여 ML 기법과 프로세스에 익숙해지는 것이 좋습니다.
[[["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)"],[[["\u003cp\u003eForecasting involves analyzing historical data to predict future trends, such as using past sales data to forecast future sales at store locations.\u003c/p\u003e\n"],["\u003cp\u003eIn BigQuery ML, forecasting is performed on time series data, which are data points collected over time.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eML.FORECAST\u003c/code\u003e function, along with the \u003ccode\u003eARIMA_PLUS\u003c/code\u003e and \u003ccode\u003eARIMA_PLUS_XREG\u003c/code\u003e models, are used to forecast future values for single or multiple variables, respectively.\u003c/p\u003e\n"],["\u003cp\u003eTime series modeling in BigQuery ML is a pipeline consisting of multiple models and algorithms.\u003c/p\u003e\n"],["\u003cp\u003eWhile deep ML knowledge is not mandatory, having a foundational understanding can help optimize your data and model to improve results.\u003c/p\u003e\n"]]],[],null,["# Forecasting overview\n====================\n\nForecasting is a technique where you analyze historical data in order to make an\ninformed prediction about future trends. For example, you might analyze\nhistorical sales data from several store locations in order to predict future\nsales at those locations. In BigQuery ML, you perform forecasting on\n[time series](https://en.wikipedia.org/wiki/Time_series) data.\n\nYou can perform forecasting in the following ways:\n\n- By using the [`AI.FORECAST` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast) with the built-in [TimesFM model](/bigquery/docs/timesfm-model). Use this approach when you need to forecast future values for a single variable, and don't require the ability to fine-tune the model. This approach doesn't require you to create and manage a model.\n- By using the [`ML.FORECAST` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-forecast) with the [`ARIMA_PLUS` model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series). Use this approach when you need to run an ARIMA-based modeling pipeline and decompose the time series into multiple components in order to explain the results. This approach requires you to create and manage a model.\n- By using the `ML.FORECAST` function with the [`ARIMA_PLUS_XREG` model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-multivariate-time-series). Use this approach when you need to forecast future values for multiple variables. This approach requires you to create and manage a model.\n\n`ARIMA_PLUS` and `ARIMA_PLUS_XREG` time series models aren't actually single\nmodels, but rather a time series modeling pipeline that includes multiple\nmodels and algorithms. For more information, see\n[Time series modeling pipeline](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#modeling-pipeline).\n\nCompare the TimesFM and `ARIMA` models\n--------------------------------------\n\nUse the following table to determine whether to use `AI.FORECAST` with the\nbuilt-in TimesFM model or `ML.FORECAST` with an `ARIMA_PLUS` or\n`ARIMA_PLUS_XREG` model for your use case:\n\nRecommended knowledge\n---------------------\n\nBy using the default settings of BigQuery ML's statements and\nfunctions, you can create and use a forecasting model even\nwithout much ML knowledge. However, having basic knowledge about\nML development, and forecasting models in particular,\nhelps you optimize both your data and your model to\ndeliver better results. We recommend using the following resources to develop\nfamiliarity with ML techniques and processes:\n\n- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)\n- [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)\n- [Time Series](https://www.kaggle.com/learn/time-series)"]]
