时间序列预测 in English translation

• time series forecasting
• time series prediction
• time-series forecasting

Examples of using 时间序列预测 in Chinese and their translations into English

CNN学习和自动从原始输入数据中提取特征的能力，可以应用于时间序列预测问题。
The ability of CNNs to learn and automatically extract features from raw input data can be applied to time series forecasting problems.

除非你的数据问题非常具体，否则许多主要问题非常相似，比如：分类、回归、时间序列预测、建议。
Unless your data problem is very specific, many of the major ones are quite similar, classification, regression, time series predictions, recommendations.

到目前为止，人们已经目睹图像识别、机器翻译、机器转录、时间序列预测取得了巨大的进步。
So far, we have seen huge advances in image recognition, machine translation, machine transcription, and time series predictions.

为此，我们将使用Keras来训练和评估时间序列预测问题的模型。
For this purpose, we will train and evaluate models for time-series prediction problem using Keras.

使用神经网络解决时间序列预测问题的好处是网络可以在获得新数据时对权重进行更新。
A benefit of using neural network models for time series forecasting is that the weights can be updated as new data becomes available.

但在这篇文章中，我将讨论时间序列预测中机器学习的一些常见缺陷。
But in this post, I will discuss some of the common pitfalls of machine learning for time series forecasting.

这为时间序列预测带来极大益处，因为经典线性方法难以适应多变量或多输入预测问题。
This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems.

有许多种方法可以进行时间序列预测，我们将在这一节中对它们做简要地介绍。
There are a number of methods for time series forecasting and we will briefly cover them in this section.

现在我们已经转换和探索了我们的数据，接下来我们继续使用ARIMA进行时间序列预测。
Now that we have converted and explored our data, let's move on to time series forecasting with ARIMA.

结果表明，我们可以使用变换器进行时间序列预测。
The results show that it would be possible to use the Transformer architecture for time-series forecasting.

Get_prediction()和conf_int()属性允许我们获得时间序列预测的值和相关的置信区间。
The get_prediction() and conf_int() attributes allow us to obtain the values and associated secret intervals for forecasts of the time series.

这是一个简单的序列预测问题，一旦被理解，就可以被推广到其他的序列预测问题，如时间序列预测和序列分类。
This is a simple sequence prediction problem that once understood can be generalized to other sequence prediction problems like time series prediction and sequence classification.

一步的单变量预测在时间序列预测中，使用滞后观察（比如t-1）作为输入变量来预测当前时间不，是通用做法。
It is standard practice in time series forecasting to use lagged observations(e.g. t-1) as input variables to forecast the current time step(t).

循环神经网络建模能力：循环神经网络（RNNs）被用于语音识别，时间序列预测，图像描述和其他需要处理时序信息的任务。
RNN Modeling Capability: Recurrent neural networks(RNNs) are used for speech recognition, time series prediction, image captioning, and other tasks that require processing sequential information.

但是，我们正在研究神经网络和更复杂的应用（如图像/模式识别，时间序列预测，函数逼近和聚类）的监督学习。
However, we're looking into supervised learning with neural networks and more complex applications like image/pattern recognition, time series prediction, function approximation, and clustering.

使用时间序列预测.
Forecasting Economic Time Series Using Targeted Predictors.

ARIMA时间序列预测.
Seasonal ARIMA Forecasts.

在这篇文章中，您将发现时间序列预测。
Inside this post, you will see time series forecasting.

在本教程中，我们将提供可靠的时间序列预测。
In this tutorial, we will aim to produce reliable forecasts of time series.

像深度学习这样的机器学习方法可以用于时间序列预测。
Machine learning methods like deep learning can be used for time series forecasting.