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@ -5,6 +5,27 @@ description: 预测算法
时序数据预测处理以持续一个时间段的时序数据作为输入，预测接下来一个连续时间区间内时间序列数据趋势。用户可以指定输出的（预测）时间序列数据点的数量，因此其输出的结果行数不确定。为此，TDengine 使用新函数 `FORECAST` 提供时序数据预测服务。基础数据（用于预测的历史时间序列数据）是该函数的输入，预测结果是该函数的输出。用户可以通过 `FORECAST` 函数调用 Anode 提供的预测算法提供的服务。
在后续章节中，使用时序数据表`foo`作为示例，介绍预测和异常检测算法的使用方式，`foo` 表的模式如下：
|列名称|类型|说明|
|---|---|---|
|ts| timestamp| 主时间戳列|
|i32| int32| 4字节整数，设备测量值 metric|
```bash
taos> select * from foo;
ts | k |
========================================
2020-01-01 00:00:12.681 | 13 |
2020-01-01 00:00:13.727 | 14 |
2020-01-01 00:00:14.378 | 8 |
2020-01-01 00:00:15.774 | 10 |
2020-01-01 00:00:16.170 | 16 |
2020-01-01 00:00:17.558 | 26 |
2020-01-01 00:00:18.938 | 32 |
2020-01-01 00:00:19.308 | 27 |
```
### 语法
```SQL
FORECAST(column_expr, option_expr)
@ -54,16 +75,16 @@ FROM foo;
taos> select _flow, _fhigh, _frowts, forecast(i32) from foo;
_flow | _fhigh | _frowts | forecast(i32) |
========================================================================================
10.5286684 | 41.8038254 | 2020-01-01 00:01:35.001 | 26 |
-21.9861946 | 83.3938904 | 2020-01-01 00:01:36.001 | 30 |
-78.5686035 | 144.6729126 | 2020-01-01 00:01:37.001 | 33 |
-154.9797363 | 230.3057709 | 2020-01-01 00:01:38.001 | 37 |
-253.9852905 | 337.6083984 | 2020-01-01 00:01:39.001 | 41 |
-375.7857971 | 466.4594727 | 2020-01-01 00:01:40.001 | 45 |
-514.8043823 | 622.4426270 | 2020-01-01 00:01:41.001 | 53 |
-680.6343994 | 796.2861328 | 2020-01-01 00:01:42.001 | 57 |
-868.4956665 | 992.8603516 | 2020-01-01 00:01:43.001 | 62 |
-1076.1566162 | 1214.4498291 | 2020-01-01 00:01:44.001 | 69 |
10.5286684 | 41.8038254 | 2020-01-01 00:01:35.000 | 26 |
-21.9861946 | 83.3938904 | 2020-01-01 00:01:36.000 | 30 |
-78.5686035 | 144.6729126 | 2020-01-01 00:01:37.000 | 33 |
-154.9797363 | 230.3057709 | 2020-01-01 00:01:38.000 | 37 |
-253.9852905 | 337.6083984 | 2020-01-01 00:01:39.000 | 41 |
-375.7857971 | 466.4594727 | 2020-01-01 00:01:40.000 | 45 |
-514.8043823 | 622.4426270 | 2020-01-01 00:01:41.000 | 53 |
-680.6343994 | 796.2861328 | 2020-01-01 00:01:42.000 | 57 |
-868.4956665 | 992.8603516 | 2020-01-01 00:01:43.000 | 62 |
-1076.1566162 | 1214.4498291 | 2020-01-01 00:01:44.000 | 69 |
```