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Model TimesFM
Dokumen ini menjelaskan model perkiraan deret waktu TimesFM bawaan BigQuery ML.
Model univariat TimesFM bawaan adalah penerapan
model TimesFM open source dari Google Research. Model TimesFM Google Research adalah model dasar untuk perkiraan deret waktu yang telah dilatih pada miliaran titik waktu dari banyak set data dunia nyata, sehingga Anda dapat menerapkannya ke set data perkiraan baru di berbagai domain.
Model TimesFM tersedia di semua region yang didukung BigQuery.
Dengan menggunakan model TimesFM bawaan BigQuery ML dengan
fungsi AI.FORECAST,
Anda dapat melakukan
peramalan tanpa harus membuat dan melatih model sendiri, sehingga Anda dapat
menghindari kebutuhan akan pengelolaan model.
Hasil perkiraan dari model TimesFM sebanding dengan metode statistik konvensional seperti ARIMA. Jika menginginkan opsi penyesuaian model yang lebih banyak daripada yang ditawarkan model TimesFM, Anda dapat membuat model
ARIMA_PLUS
atau
ARIMA_PLUS_XREG
dan menggunakannya dengan
fungsi ML.FORECAST.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-17 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)"]]
