时间序列 英语是什么意思 - 英语翻译

• time series
• time sequence
• timeseries
• time-series
• temporal sequence

在 中文 中使用 时间序列 的示例及其翻译为 英语

时间序列预测.
The Time Sequence Forecasting.

MSU-TMT（全球和热带手段）（时间序列和趋势）.
MSU-TMT(global and tropical means)(timeseries and trends).

MSU-TMT（全球和热带平均数）（时间序列和趋势）.
MSU-TMT(global and tropical means)(timeseries and trends).

已经提出STDP模型来解释空间和时间序列学习。
The STDP model has been proposed to explain spatial and temporal sequence learning.

时间序列分析:预测与控制.
Times Series Analysis: Forecasting and Control.

这基本上是时间序列对自身的回归。
This is basically a regression of the time series onto itself.

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.

为什么时间序列数据库现在很重要?
Why is a Time Series Database Important Now?

平稳性是时间序列的重要特征。
Stationarity is an important characteristic of time series.

时间序列只是按时间顺序排列的一系列数据点。
A time series is simply a series of data points ordered in time..

时间序列预测的七种方法(附python代码).
Techniques for time series forecasting(with python codes).

时间序列据库.
A Time Series Database.

这种时间序列叫做白噪声。
We call such a time series white noise.

时间序列中缺少的值被视为0。
Missing values in time series are treated as 0.

使用逻辑回归方法预测时间序列在未来的结果；.
Use a Logistic Regression scheme to predict the future result of a time series.

平稳性是时间序列的一个重要特征。
Stationarity is an important characteristic of time series.

LSTM做时间序列预测的问题.
I am implementing LSTM for time series forecasting.

这基本上是时间序列回归到自身。
This is basically a regression of the time series onto itself.

这是时间序列最基本的概念。
This is the most basic concept of the time series.

有很多方法可以模拟时间序列来进行预测。
Several models can be used for time-series forecasting.

此外，我们还讨论了时间序列建模的实际应用。
In addition, we will also discuss about the practical applications of time series modelling.

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.

最后一步，分解并绘制时间序列。
In the last step, we decompose and plot the time series.

一、什么是时间序列？?
What's a time series?

要分析这些问题，你需要比较时间序列之间的距离。
To analyse this, you have to compare the distance between time series.

多个刻度可以一起更好地理解时间序列。
Then I can have a better understanding of the time series.

多个刻度可以一起更好地理解时间序列。
Together, multiple scales can deliver a better understanding of the time series.

深度学习，时间序列预测.
Deep Learning for Time Series Forecasting.

这基本上是对时间序列的回归。
This is basically a regression of the time series onto itself.

金融数据属于时间序列数据。
And financial data is a time-series data.