先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
编辑构造材料（未运行）：
表 daily_sales 包含 day DATE、revenue FLOAT64，每日一行。需要预测接下来 7 天收入，不需要解释营销活动影响。
输出
编辑示例 · 未运行
建议任务：时间序列预测。
候选路径：读取 AI.FORECAST 参考，将 day 作为时间列、revenue 作为数值序列，预测长度设为 7。
排除：当前问题没有干预时点和对照要求，不应直接套用 AI.CAUSAL_EFFECT。
执行前检查：日期连续性、空值、历史长度以及函数可用区域。当前没有预测数值。
方法说明
---
name: bigquery-ai-ml
metadata:
version: "1.1.0"
category: AiAndMachineLearning
description: >-
Leverages BigQuery's built-in machine learning and GenAI capabilities
for advanced data analytics. Use when you need to write SQL queries
that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers,
perform semantic search or vector search, classify text, calculate similarity,
summarize content, translate language, evaluate models, filter by semantic conditions,
measure the causal effect of an intervention, compute correlations between columns,
detect change points or structural breaks, extract trend or seasonality components,
or leverage generative AI capabilities in BigQuery. Do not use for general
BigQuery dataset, table, or job management requests.
---
# BigQuery AI & ML
BigQuery integrates with Vertex AI to provide powerful machine learning and
generative AI capabilities directly within SQL queries using built-in functions
like `AI.FORECAST`, `AI.KEY_DRIVERS`, `AI.DETECT_ANOMALIES`, and `AI.GENERATE`.
## Reference Directory
- **Functions Reference**:
- **AI.AGG**: [ai_agg.md](references/ai_agg.md) - Multi-row semantic
aggregation and summarization.
- **AI.CAUSAL_EFFECT**:
[ai_causal_effect.md](references/ai_causal_effect.md) - Quantifies the
impact of an intervention on a time series.
- **AI.CLASSIFY**: [ai_classify.md](references/ai_classify.md) - Classify
text.
- **AI.DETECT_ANOMALIES**:
[ai_detect_anomalies.md](references/ai_detect_anomalies.md) - Detect
anomalies.
- **AI.EVALUATE**: [ai_evaluate.md](references/ai_evaluate.md) - Evaluate
models.
- **AI.FORECAST**: [ai_forecast.md](references/ai_forecast.md) -
Time-series forecasting.
- **AI.GENERATE**: [ai_generate.md](references/ai_generate.md) - Generate
text using LLMs.
- **AI.GENERATE_EMBEDDING**:
[ai_generate_embedding.md](references/ai_generate_embedding.md) -
Generate embeddings.
- **AI.GENERATE_TABLE**:
[ai_generate_table.md](references/ai_generate_table.md) - Table-valued
AI generation.
- **AI.IF**: [ai_if.md](references/ai_if.md) - Evaluate semantic
conditions.
- **AI.KEY_DRIVERS**: [ai_key_drivers.md](references/ai_key_drivers.md) -
Identifies key drivers, this is a TVF.
- **AI.SCORE**: [ai_score.md](references/ai_score.md) - Score data.
- **AI.SEARCH**: [ai_search.md](references/ai_search.md) - Semantic
search.
- **AI.SIMILARITY**: [ai_similarity.md](references/ai_similarity.md) -
Semantic similarity.
- **Remote Models**: [remote_models.md](references/remote_models.md) -
Working with remote models (Vertex AI).
- **CONTRIBUTION_ANALYSIS**:
[ml_contribution_analysis.md](references/ml_contribution_analysis.md)
- Finds contributing factors, key drivers of change. Requires creating
a MODEL entity.
- **ML.CORRELATION**: [ml_correlation.md](references/ml_correlation.md) -
Calculates correlation between columns, optionally sliced by dimensions.
- **ML.DETECT_CHANGE_POINTS**:
[ml_detect_change_points.md](references/ml_detect_change_points.md) -
Detects structural breaks or sustained shifts in a time series.
- **ML.SEASONALITY**: [ml_seasonality.md](references/ml_seasonality.md) -
Extracts seasonal components from a time series.
- **ML.TREND**: [ml_trend.md](references/ml_trend.md) - Extracts the
long-term trend component from a time series.
- **VECTOR_SEARCH**: [vector_search.md](references/vector_search.md) -
Vector search best practices.
## Related Skills
- [BigQuery Basics Skill](../bigquery-basics): SKILL.md file for core BigQuery
concepts, resource management, CLI, and client libraries.