指定目标列。借助目标列，Vertex AI 可以将训练数据与所需结果相关联。该值不得包含 null 值，并且必须是数值。
时间列
必须指定一个时间列，并且每行都必须有一个值。时间列表示进行给定观察的时间。
时序标识符列
必须指定一个时序标识符列，并且每行必须包含一个值。预测训练数据通常包含多个时序，标识符可让 Vertex AI 了解训练数据中给定观察结果属于哪个时序。给定时序中的所有行在时序标识符列中具有相同的值。一些常见的时序标识符可能是商品 ID、商店 ID 或区域。您可以在单个时序上训练预测模型，并且时序标识符列中的所有行都具有相同的值。但是，Vertex AI 更适合包含两个或更多时序的训练数据。为获得最佳结果，请对用于训练模型的每一列至少使用 10 个时序。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-08-25。"],[],[],null,["# Prepare training data\n\nThis page describes how to prepare your tabular data for training forecast models.\n\n\u003cbr /\u003e\n\nThis page covers the following topics:\n\n1. [Data structure requirements](#data-structure)\n2. [Prepare your import source](#import-source)\n3. [Add weights to your training data](#weight)\n\nBy default, Vertex AI uses a\n[chronological split](/vertex-ai/docs/tabular-data/data-splits#forecasting-default)\nalgorithm to separate your forecasting data into three data splits.\nAlternatively, you can use a\n[manual split](/vertex-ai/docs/tabular-data/data-splits#forecasting-manual), but\nthis requires you to prepare a data split column.\n[Learn more](/vertex-ai/docs/tabular-data/data-splits) about data splits.\n\nData structure requirements\n---------------------------\n\nFor forecasting models, your training data must conform to the following\nbasic requirements:\n\nPrepare your import source\n--------------------------\n\nYou can provide model training data to Vertex AI in two formats:\n\n- BigQuery tables\n- Comma-separated values (CSV)\n\nThe source you use depends on how you store your data, and the size and\ncomplexity of your data. If your dataset is small, and you don't need more\ncomplex data types, CSV might be easier. For larger datasets that include arrays\nand structs, use BigQuery. \n\n### BigQuery\n\nYour BigQuery table or view must conform to the\n[BigQuery location requirements](/vertex-ai/docs/general/locations#bq-locations).\n\nIf your BigQuery table or view is in a different project than the\nproject where you are creating your Vertex AI dataset, or your\nBigQuery table or view is backed by an external data source, add\none or more roles to the Vertex AI Service Agent. See\n[Role addition requirements for BigQuery](/vertex-ai/docs/general/access-control#bq-roles).\n\nYou don't need to specify a schema for your BigQuery table.\nVertex AI automatically infers the schema for your table when you\nimport your data.\n\nYour BigQuery URI (specifying the location of your training data)\nmust conform to the following format: \n\n```\nbq://\u003cproject_id\u003e.\u003cdataset_id\u003e.\u003ctable_id\u003e\n```\n\nThe URI cannot contain any other special characters.\n\nFor information about BigQuery data types and how they map into\nVertex AI, see [BigQuery tables](/vertex-ai/docs/datasets/data-types-tabular#bq). For more\ninformation about using BigQuery external data sources, see\n[Introduction to external data sources](/bigquery/external-data-sources).\n\n### CSV\n\nCSV files can be in Cloud Storage, or on your local computer. They must\nconform to the following requirements:\n\n- The first line of the first file must be a header, containing the names of the columns. If the first row of a subsequent file is the same as the header, then it is also treated as a header, otherwise it's treated as data.\n- Column names can include any alphanumeric character or an underscore (_). The column name cannot begin with an underscore.\n- Each file must not be larger than 10 GB.\n\n You can include multiple files, up to a maximum amount of 100 GB.\n- The delimiter must be a comma (\",\").\n\nYou don't need to specify a schema for your CSV data. Vertex AI\nautomatically infers the schema for your table when you import your data, and\nuses the header row for column names.\n\nFor more information about CSV file format and data types, see\n[CSV files](/vertex-ai/docs/datasets/data-types-tabular#csv).\n\nIf you import your data from Cloud Storage, it must be in a\nbucket that meets the following requirements:\n\n- It conforms to the [Vertex AI bucket requirements](/vertex-ai/docs/general/locations#buckets).\n- If the bucket is not in the same project as Vertex AI, add one or more roles to the Vertex AI Service Agent. See [Role addition requirements for Cloud Storage](/vertex-ai/docs/general/access-control#storage-roles).\n\nIf you import your data from your local computer, you must have a\nCloud Storage bucket that meets the following requirements:\n\n- It conforms to the [Vertex AI bucket requirements](/vertex-ai/docs/general/locations#buckets).\n- If the bucket is not in the same project as Vertex AI,\n add one or more roles to the Vertex AI Service Agent.\n See [Role addition requirements for Cloud Storage](/vertex-ai/docs/general/access-control#storage-roles).\n\n Vertex AI uses this bucket as a staging area before importing\n your data.\n\nAdd weights to your training data\n---------------------------------\n\nBy default, Vertex AI weighs each row of your training data\nequally. For training purposes, no row is considered more important than\nanother.\n\nSometimes, you might want some rows to have more importance for training. For\nexample, if you use spending data, you might want the data associated with\nhigher spenders to have a larger impact on the model. If you particularly want\nto avoid missing a specific outcome, then weight\nrows with that outcome more heavily.\n\nAdd a weight column to your dataset to give rows a relative weight. The\nweight column must be a numeric column. The weight value can be 0‑10,000.\nHigher values indicate that the row is more important when training the model. A\nweight of 0 causes the row to be ignored. If you include a weight column, it\nmust contain a value for every row.\n\nLater, when you train your model, specify this column as the `Weight`\ncolumn.\n\nCustom weighting schemes are used only for training the model; they do not\naffect the test set used for model evaluation.\n\nWhat's next\n-----------\n\n- [Create your dataset](/vertex-ai/docs/tabular-data/forecasting/create-dataset).\n- Learn about [best practices for creating tabular training data](/vertex-ai/docs/tabular-data/bp-tabular).\n- Learn how [Vertex AI works with different types of tabular data](/vertex-ai/docs/datasets/data-types-tabular)."]]
