> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Predictive Query Structure
A **Predictive Query** defines a predictive modeling task in Kumo using **PQL (Predictive Query Language)**, a SQL-like syntax that specifies:
* **Target** – What you want to predict.
* **Entity** – Who you are making predictions for.
* **Filters (optional)** – Constraints on which entities or data to include.
## Target
The **target** is the outcome you want to predict, defined after the `PREDICT` command.
For example, to predict **total purchases per user over the next 30 days**, the target is **"sum of purchases over the next 30 days."**
## Entity
The **entity** is the subject of your prediction—**who** the prediction is being made for.
For example, if predicting **total purchases per user**, then the **user** is the entity.
## Aggregation Operators
When predicting an **aggregation over time** (e.g., total sales over 30 days), use an **aggregation function** with a column reference.
**Example: Predicting Total Purchase Value per Customer**
```Text PQL theme={null}
PREDICT SUM(TRANSACTIONS.PRICE, 0, 30)
FOR EACH CUSTOMERS.CUSTOMER_ID
```
* `SUM(TRANSACTIONS.PRICE, 0, 30)` → Sums purchase values over the next **30 days**.
* `FOR EACH CUSTOMERS.CUSTOMER_ID` → Predicts **for each customer**.
The above usage of the `SUM()` aggregation operator allows you to predict the total value of purchases each customer will make in the next 30 days.
Within the aggregation function inputs, the start and end parameters refer to the time period you want to aggregate across, calculated in days. For example, 10 for start and 30 for end implies that you want to aggregate from 10 days later (excluding the 10th day) to 30 days later (including the 30th day). The time unit of the aggregation defaults to 'days' if none is specified.
If you're making the prediction on `2020-01-01 00:00:00`, Kumo will aggregate all rows with timestamps *t* where `2020-01-11 00:00:00 < t <= 2020-01-31 00:00:00`.
When using aggregation with targets, both *start* and *end* values should be non-negative integers, and *end* values should be greater than start values.
### Common Aggregation Functions
* `SUM()` – Total value over time.
* `COUNT()` – Number of occurrences over time.
For a complete list of aggregation functions and further details, please refer to [Aggregation Operators](/reference/avg)
### Aggregation Window (Start & End)
* The **start** and **end** parameters define the **prediction window** in days.
* If the prediction date is `2020-01-01`:
* `10, 30` will predict transaction values from **`2020-01-11 to 2020-01-31`**.
```Text PQL theme={null}
PREDICT SUM(TRANSACTIONS.PRICE, 10, 30)
FOR EACH CUSTOMERS.CUSTOMER_ID
```
### Aggregation Units
The time unit defaults to **days**, but can also be:
* `days` (default)
* `months`
* `hours`
```Text PQL theme={null}
PREDICT SUM(TRANSACTIONS.PRICE, 0, 30, months)
FOR EACH CUSTOMERS.CUSTOMER_ID
```
## Filters (`WHERE`)
Filters refine a Predictive Query by **removing irrelevant entities or restricting aggregation conditions**.
For example, to **predict purchases for active customers only** (i.e., those who made at least one transaction in the past 30 days):
```Text PQL theme={null}
PREDICT SUM(TRANSACTIONS.PRICE, 0, 30)
FOR EACH CUSTOMERS.CUSTOMER_ID
WHERE COUNT(TRANSACTIONS.*, -30, 0) > 0
```
Kumo supports **advanced filtering**, including:
* Inline filters inside aggregations
* Nested temporal filters
* Static date/time filters
* Multiple target conditions (`AND`/`OR`)
For a complete guide to filtering, see [**Predictive Query Reference**](/reference/where).