时间序列预测 时间因果建模

时间序列-建模 (Time Series - Modeling)

介绍 (Introduction)

A time series has 4 components as given below −

时间序列具有4个成分，如下所示-

• Level − It is the mean value around which the series varies.

  水平 -它是序列变化的平均值。

• Trend − It is the increasing or decreasing behavior of a variable with time.

  趋势 -它是变量随时间的增加或减少行为。

• Seasonality − It is the cyclic behavior of time series.

  季节性 -这是时间序列的周期性行为。

• Noise − It is the error in the observations added due to environmental factors.

  噪声 -这是由于环境因素导致的观测值误差。

时间序列建模技术 (Time Series Modeling Techniques)

To capture these components, there are a number of popular time series modelling techniques. This section gives a brief introduction of each technique, however we will discuss about them in detail in the upcoming chapters −

为了捕获这些组件，有许多流行的时间序列建模技术。 本节简要介绍了每种技术，但是我们将在接下来的章节中详细讨论它们-

幼稚的方法 (Naïve Methods)

These are simple estimation techniques, such as the predicted value is given the value equal to mean of preceding values of the time dependent variable, or previous actual value. These are used for comparison with sophisticated modelling techniques.

这些是简单的估算技术，例如，给预测值等于时间相关变量的先前值或先前实际值的平均值。 这些用于与复杂的建模技术进行比较。

自回归 (Auto Regression)

Auto regression predicts the values of future time periods as a function of values at previous time periods. Predictions of auto regression may fit the data better than that of naïve methods, but it may not be able to account for seasonality.

自动回归功能将根据先前时间段的值来预测未来时间段的值。 自回归的预测可能比朴素的方法更适合数据，但可能无法说明季节性。

ARIMA模型 (ARIMA Model)

An auto-regressive integrated moving-average models the value of a variable as a linear function of previous values and residual errors at previous time steps of a stationary timeseries. However, the real world data may be non-stationary and have seasonality, thus Seasonal-ARIMA and Fractional-ARIMA were developed. ARIMA works on univariate time series, to handle multiple variables VARIMA was introduced.

自回归积分移动平均值将变量的值建模为固定时间序列的先前时间步长上先前值和残差的线性函数。 但是，现实世界的数据可能是不稳定的，并且具有季节性，因此开发了Seasonal-ARIMA和Fractional-ARIMA。 ARIMA研究单变量时间序列，以处理多个变量。

指数平滑 (Exponential Smoothing)

It models the value of a variable as an exponential weighted linear function of previous values. This statistical model can handle trend and seasonality as well.

它将变量的值建模为先前值的指数加权线性函数。 该统计模型还可以处理趋势和季节性。

LSTM (LSTM)

Long Short-Term Memory model (LSTM) is a recurrent neural network which is used for time series to account for long term dependencies. It can be trained with large amount of data to capture the trends in multi-variate time series.

长短期记忆模型(LSTM)是一个递归神经网络，用于时间序列以解决长期依赖性。 可以使用大量数据对其进行训练，以捕获多元时间序列中的趋势。

The said modelling techniques are used for time series regression. In the coming chapters, let us now explore all these one by one.

所述建模技术用于时间序列回归。 在接下来的章节中，让我们现在逐一探讨所有这些。

翻译自: https://www.tutorialspoint.com/time_series/time_series_modeling.htm

时间序列预测 时间因果建模