The team — basic TimeSeries structures.
球队——基本TimeSeries结构。
The second parameter is the TimeSeries table name.
第二个参数（以黑体显示）是TimeSeries表名。
Each TimeSeries column can have a different origin.
每个timeseries列可以有一个不同的起点。
Now the data must be loaded into the TimeSeries table.
现在必须将数据装载到TimeSeries表中。
These types of TimeSeries queries are not easy to write.
这些类型的TimeSeries查询不容易编写。
Notice that a table may have multiple TimeSeries columns.
注意，一个表可能有多个TimeSeries列。
In the baseball example below, I use a regular TimeSeries.
在下面关于棒球的例子中，我将使用定时TimeSeries。
There are four basic structures for building a TimeSeries.
有四种基本的结构可用于构建TimeSeries。
Virtual tables can also be used for loading TimeSeries data.
虚表还可以用于装载TimeSeries数据。
You must decide on the type of TimeSeries before creating it.
在创建TimeSeries之前，必须确定它的类型。
A regular TimeSeries has data at known and defined time intervals.
定时TimeSeries包含已知的、定义好的时间间隔内的数据。
The final basic structure for building TimeSeries data is the Container.
最后一种用于构建TimeSeries数据的基本结构是Container。
The User's Guide describes many other ways to load data into a TimeSeries.
User ' sGuide中描述了将数据装载到TimeSeries的很多其他方法。
There are some remedies, but first, let's look at some of the power of TimeSeries.
对此有一些解决办法，但是首先我们还是来看TimeSeries的一些威力。
Notice that the example creates a separate virtual table for each TimeSeries column.
注意，这个例子为每个timeseries列创建一个单独的虚表。
For speed, though, you must dig in and build the more complicated TimeSeries queries.
但是，要获得更好的速度，则必须精心构造更复杂的TimeSeries查询。
Otherwise, you have to build a new ROW type and a new table to hold an updated TimeSeries schema.
否则，就不得不构建一个新的row类型和一个新表来存放更新的TimeSeries模式。
The Informix TimeSeries DataBlade was designed to help Informix users better handle time-related data.
InformixTimeSeriesDataBlade设计用于帮助Informix用户更好地处理与时间相关的数据。
In my tests, it can be an order of magnitude slower than a TimeSeries function that does the same query.
在我的测试中，虚表上的查询比做同样查询的TimeSeries函数要慢一个数量级。
This is a user-defined datatype that defines the columns or elements of data to be stored in the TimeSeries.
这是一种用户定义数据类型，它定义了存储在TimeSeries中的列或数据的元素。
After seeing the TimeSeries functions and casts and other elements, a virtual table looks like an easy choice.
看过了TimeSeries函数和强制类型转换以及其他元素之后，虚表看上去是一种自然的选择。
If, however, your data is capturing live stock trade transactions, it would likely be an irregular TimeSeries.
但是，如果您的数据是捕捉股票交易事务，那么它很可能就是非定时TimeSeries。
TimeSeries data is more compact and faster to access precisely because it does not actually store the date and time with the data.
TimeSeries数据更小一些，并且访问起来也更快一些，因为它并不是存储真正的日期和时间数据。
It can be very slow to do queries across a virtual table, especially if you use one of the TimeSeries elements in the WHERE clause.
虚表上的查询可能非常慢，尤其是在where子句中使用某个TimeSeries元素时更是如此。
Also, I have occasionally encountered strange results from virtual table queries that do not occur when I use the direct TimeSeries functions.
而且，虚表查询偶尔还会返回奇怪的结果，而在使用TimeSeries函数时不会碰到这种情况。
Because of the difficulty of migrating TimeSeries data and schemas, TimeSeries is best used with known data schemas that are unlikely to change.
由于TimeSeries数据和模式的迁移比较困难，TimeSeries最好和已知的不大变化的数据模式一起使用。
The TimeSeries DataBlade provides another tool to cut through some of the complexity of working with TimeSeries data -- routines to build virtual tables.
TimeSeriesDataBlade提供另一种工具来降低处理 TimeSeries数据的复杂性 —— 这就是用于构建虚表的例程。
Another limitation is that you cannot create indexes on TimeSeries virtual tables. Listing 11 shows how to build virtual tables for the baseball example.
另一个限制是，不能在TimeSeries虚表上创建索引。
In mathematical programming, the goal of coding is often the standard output values that the analytical procedure (such as MultipleRegression, TimeSeries, or ChiSquared) is expected to generate.
在数学编程中，编码的目标通常是分析过程(比如MultipleRegression、TimeSeries或ChiS qu ared)所希望生成的标准输出值。
In mathematical programming, the goal of coding is often the standard output values that the analytical procedure (such as MultipleRegression, TimeSeries, or ChiSquared) is expected to generate.
在数学编程中，编码的目标通常是分析过程(比如MultipleRegression、TimeSeries或ChiS qu ared)所希望生成的标准输出值。
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