时间序列模型 英语是什么意思 - 英语翻译

• time series model
• time series models

在 中文 中使用 时间序列模型 的示例及其翻译为 英语

结构时间序列模型能应用在各种时间序列的问题。
Structural time series modelling can be applied to a variety of problems in time series..

结构时间序列模型能应用在各种时间序列的问题。
Structural time series modelling can be applied to a range of issues in time collection.

此外，还将讨论时间序列模型的实际应用。
In addition, we will also discuss about the practical applications of time series modelling.

此外,在预测市场的同时,还为每个市场设计了一个全面的统计时间序列模型。
Moreover, while forecasting the market a comprehensive statistical time series model is designed for each market.

在时间序列模型中，我们通常使用一段时期的数据训练，然后用另一段时期的数据进行测试。
In time series models, we generally train on one period of time and then test on another separate period.

在本教程的最后一步，我们将介绍如何利用季节性ARIMA时间序列模型来预测未来的价值。
In the closing step of this tutorial, we describe how to leverage our seasonal ARIMA time series model to forecast future values.

在时间序列模型中，通常会在一段时间内进行训练，在另外一段时间内单独进行测试。
In time series models, we generally train on one period of time and then test on another separate period.

现在，我将向大家介绍一个构建时间序列模型的全面框架。
Now, I will introduce you to a comprehensive framework to build a time series model.

标准时间序列模型使用重复出售指数表明,积极的趋势有一个半衰期长。
Standard time series models using repeat-sales indices suggested that positive trends had a long half-life.

但简而言之，计量经济学在本质上是非常统计的，使用时间序列模型，如自回归过程。
But in short, econometrics is heavily statistical in nature, using time series models such as auto-regressive processes.

拟合时间序列模型，强制让任何激活特征在任何时候都是激活的.
Fitting a time-series model, imposing that any active feature be active at all times.

这涉及算法、空间和时间序列模型、地理编码系统和统计估算程序的开发。
This involves development of algorithms, spatial and time-series models, geo-coding systems and statistical estimation processes.

在本章中，我们将探讨时间序列模型的两种主要应用：解释过去和预测未来。
In this chapter, we explore the two chief uses of time-series models: to explain the past and to predict the future of a time series.

这包含了"酱"类型的影响，如时间序列模型和民意测验专家效果。
This incorporates the“special sauce” types of influence, such as time-series model and pollster effects.

我们可以看出这个序列是足够平稳做任何时间序列模型。
We see that the series is stationary enough to do any kind of time series modelling.

在时间序列模型中，探索数据是最重要的一步--如果没有这一步，你将不知道这个序列是不是平稳序列。
Exploring data becomes most important in a time series model- without this exploration, you will not know whether a series is stationary or not.

在本节中，我们将通过编写Python代码来编程选择ARIMA(p,d,q)(P,D,Q)s时间序列模型的最优参数值来解决此问题。
In this part, we will resolve this issue by writing Python code to programmatically choose the best parameter values for our ARIMA(p, d, q)(P, D, Q)s time series model.

时间序列模型中的季节性.
Seasonality in Time-Series Models.

ARIMA时间序列模型的框架与应用.
Framework and Applications of ARIMA time series models.

根据时间序列模型的识别规则，建立相应的模型。
(3) Establish the appropriate model according to the recognition rules based on time series model..

三）根据时间序列模型的识别规则，建立相应的模型。
(3) Establish the appropriate model according to the recognition rules based on time series model..

在R中，白噪声模型是基本的时间序列模型，这也是更详细和定义的模型的基础。
In R, a white noise model is a basic time series model which is also the basis for more elaborated and defined models..

这样，过程将为每个目标构建一个自回归时间序列模型，并且仅包括那些与目标具有因果关系的输入。
The procedure then builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target.

为了做到这一点，研究人员们使用了一种递归神经网络（RNN）对电子病历记录建立时间序列模型。
To achieve this, researchers use a recurrent neural network(RNN) to model temporal relations among events in electronic health records.

一阶自回归过程[AR（1）]（AutoregressiveProcessofOrderOne[AR(1)]）：一个时间序列模型，其当前值线性依赖于最近的值加上一个无法预测的扰动。
Autoregressive Process of Order One[AR(l)]: A time series model whose current value depends linearly on its most recent value plus an unpredictable disturbance.

时间序列ARIMA模型.
ARIMA time series models.

时间序列与动态模型.
Time series and dynamic models.

时间序列的季节模型.
Models for seasonal time series.

我们已经获得了我们时间序列的模型，现在可以用来产生预测。
We have obtained a model for our time series that can now be used to produce forecasts.

经济学，特别是宏观经济学，时间序列模型，以及计量经济学.
Economics, in particular macroeconomics, time series, and econometrics.