autoregressive moving average

简明释义

自回归滑动平均;

英英释义

例句

1.The financial analyst used an autoregressive moving average model to forecast stock prices for the next quarter.

金融分析师使用了一个自回归移动平均模型来预测下个季度的股票价格。

2.When predicting future sales, the team relied on the autoregressive moving average model to smooth out fluctuations.

在预测未来销售时，团队依赖于自回归移动平均模型来平滑波动。

3.The autoregressive moving average method helped the company identify patterns in customer behavior over time.

该自回归移动平均方法帮助公司识别客户行为随时间变化的模式。

4.Researchers applied the autoregressive moving average technique to analyze seasonal trends in sales data.

研究人员应用自回归移动平均技术分析销售数据中的季节性趋势。

5.In time series analysis, the autoregressive moving average approach is often preferred for its accuracy.

在时间序列分析中，自回归移动平均方法因其准确性而常被优先选择。

作文

In the realm of time series analysis, one of the most essential models used for forecasting and understanding data patterns is the autoregressive moving average model, often abbreviated as ARMA. This model is particularly useful when dealing with univariate time series data, where the goal is to predict future values based on past observations. To understand the significance of the autoregressive moving average model, it is crucial to break down its components: autoregression and moving average. Autoregression refers to the use of previous values in a time series to predict future values. For instance, if we are trying to forecast the temperature for tomorrow, we might look at the temperatures from the past few days to make an educated guess. The idea is that past values have a direct influence on future outcomes, which is the foundational principle of autoregression. On the other hand, the moving average component of the autoregressive moving average model helps to smooth out short-term fluctuations in the data, providing a clearer view of the underlying trend. It does this by averaging the values of a specified number of previous data points. By combining these two elements, the autoregressive moving average model becomes a powerful tool for both understanding and forecasting time series data. One of the key advantages of using the autoregressive moving average model is its ability to capture various patterns in the data. For example, it can effectively model seasonality and trends, which are common in many real-world applications such as finance, economics, and environmental studies. However, it is important to note that the autoregressive moving average model is not without its limitations. It assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. If the data exhibits trends or seasonality, it may require preprocessing steps, such as differencing or seasonal adjustments, before applying the ARMA model. Additionally, the selection of the appropriate parameters for the autoregressive moving average model can be challenging. Analysts often rely on techniques like the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) to determine the optimal order of the autoregressive and moving average components. In conclusion, the autoregressive moving average model is a fundamental tool in the field of time series analysis, enabling researchers and practitioners to forecast future values based on historical data. Its combination of autoregression and moving average components allows for a nuanced understanding of data patterns, making it applicable across various domains. Despite its limitations, when used correctly, the autoregressive moving average model can provide valuable insights and predictions, aiding decision-making processes in numerous fields.

在时间序列分析领域，用于预测和理解数据模式的最基本模型之一是自回归移动平均模型，通常缩写为ARMA。该模型在处理单变量时间序列数据时尤其有用，其目标是基于过去的观察值来预测未来的值。要理解自回归移动平均模型的重要性，必须拆解其组成部分：自回归和移动平均。自回归指的是使用时间序列中的先前值来预测未来值。例如，如果我们试图预测明天的温度，我们可能会查看过去几天的温度，以做出合理的猜测。这个想法是，过去的值对未来的结果有直接影响，这是自回归的基础原则。另一方面，自回归移动平均模型的移动平均部分有助于平滑数据中的短期波动，从而提供更清晰的潜在趋势视图。它通过对指定数量的先前数据点的值进行平均来实现这一点。通过结合这两个元素，自回归移动平均模型成为理解和预测时间序列数据的强大工具。使用自回归移动平均模型的一个关键优势是其捕捉数据中各种模式的能力。例如，它可以有效地建模季节性和趋势，这在金融、经济和环境研究等许多实际应用中很常见。然而，重要的是要注意，自回归移动平均模型并非没有其局限性。它假设基础数据是平稳的，这意味着其统计特性不会随时间变化。如果数据呈现出趋势或季节性，可能需要在应用ARMA模型之前进行预处理步骤，例如差分或季节调整。此外，选择适当的自回归移动平均模型参数可能具有挑战性。分析师通常依赖于赤池信息量准则（AIC）或贝叶斯信息量准则（BIC）等技术来确定自回归和移动平均组件的最佳顺序。总之，自回归移动平均模型是时间序列分析领域的基本工具，使研究人员和从业者能够根据历史数据预测未来值。其自回归和移动平均组件的组合使其能够深入理解数据模式，使其适用于各个领域。尽管存在局限性，但如果使用得当，自回归移动平均模型可以提供有价值的见解和预测，帮助多个领域的决策过程。