predicted value; forecast value

简明释义

预测值，预计值

英英释义

例句

1.We need to compare the actual results with the forecast value 预测值 to assess our performance.

我们需要将实际结果与预测值 预测值进行比较，以评估我们的表现。

2.The predicted value 预测值 for next quarter's sales is $1 million.

下个季度的销售预测值 预测值为100万美元。

3.The weather report indicates a predicted value 预测值 of rainfall for the weekend.

天气预报显示周末的降雨量有一个预测值 预测值。

4.According to the model, the forecast value 预测值 of temperature for tomorrow is 25°C.

根据模型，明天的温度预测值 预测值为25°C。

5.The financial analyst provided a predicted value 预测值 for the stock market growth this year.

金融分析师提供了今年股市增长的预测值 预测值。

作文

In the realm of data analysis and statistical modeling, the terms predicted value (预测值) and forecast value (预报值) are often used interchangeably, yet they can have distinct meanings depending on the context. Understanding these concepts is essential for anyone involved in research, business analytics, or any field that relies on data-driven decision-making.The predicted value refers to the output generated by a model based on input variables. For example, in a regression analysis where we are trying to predict a person's weight based on their height and age, the predicted value would be the estimated weight calculated from the model. This value is derived from the historical data used to train the model and represents what the model believes the outcome will be, given the specific inputs.On the other hand, the forecast value often relates to predictions made about future events or trends. For instance, in economics, analysts might use various models to project future sales figures for a company. The forecast value in this case would be the expected sales for the next quarter, based on current trends and historical data. While both predicted values and forecast values are estimates, the latter specifically emphasizes the time aspect, focusing on what is expected to happen in the future.Both predicted values and forecast values play critical roles in various fields. In finance, investors rely on these values to make informed decisions about buying or selling stocks. In healthcare, predictive models can help anticipate patient outcomes based on treatment plans, leading to better patient care. Moreover, businesses use these values to optimize inventory levels, manage supply chains, and enhance customer satisfaction.However, it is crucial to understand that both predicted values and forecast values come with inherent uncertainties. The accuracy of these values depends on the quality of the data and the models used. For instance, if the historical data is flawed or if the model does not accurately capture the relationships between variables, the predicted value may be significantly off. Similarly, external factors such as economic shifts, natural disasters, or changes in consumer behavior can impact the reliability of forecast values.To improve the accuracy of predicted values and forecast values, analysts often employ various techniques such as cross-validation, ensemble methods, and sensitivity analysis. These methods help in assessing the robustness of the models and ensure that the predictions made are as reliable as possible.In conclusion, while predicted values (预测值) and forecast values (预报值) are vital tools in data analysis, understanding their differences and applications is crucial for effective decision-making. By leveraging these values, organizations and individuals can navigate uncertainties and make informed choices that lead to better outcomes. As data continues to play an increasingly significant role in our lives, mastering these concepts will be invaluable for anyone looking to harness the power of data-driven insights.

在数据分析和统计建模的领域中，术语预测值和预报值通常可以互换使用，但根据上下文，它们可能具有不同的含义。理解这些概念对于任何参与研究、商业分析或依赖数据驱动决策的领域的人来说都是至关重要的。预测值是指模型根据输入变量生成的输出。例如，在回归分析中，我们试图根据一个人的身高和年龄来预测其体重，预测值将是从模型计算出的估计体重。这个值是从用于训练模型的历史数据中得出的，代表了模型在给定特定输入时认为的结果。另一方面，预报值通常与对未来事件或趋势的预测有关。例如，在经济学中，分析师可能会使用各种模型来预测公司未来的销售数字。在这种情况下，预报值将是基于当前趋势和历史数据预计的下个季度销售额。虽然预测值和预报值都是估计值，但后者特别强调时间方面，专注于未来可能发生的事情。预测值和预报值在各个领域中都扮演着关键角色。在金融领域，投资者依赖这些值来做出关于买入或卖出股票的明智决策。在医疗保健中，预测模型可以帮助预测患者根据治疗计划的结果，从而改善患者护理。此外，企业利用这些值来优化库存水平、管理供应链和提高客户满意度。然而，必须理解的是，预测值和预报值都伴随着固有的不确定性。这些值的准确性取决于所使用的数据和模型的质量。例如，如果历史数据存在缺陷，或者模型未能准确捕捉变量之间的关系，则预测值可能会显著偏离。类似地，经济变化、自然灾害或消费者行为的变化等外部因素也会影响预报值的可靠性。为了提高预测值和预报值的准确性，分析师通常采用交叉验证、集成方法和敏感性分析等各种技术。这些方法有助于评估模型的稳健性，并确保所做的预测尽可能可靠。总之，虽然预测值和预报值是数据分析中的重要工具，但理解它们的差异和应用对于有效的决策制定至关重要。通过利用这些值，组织和个人可以应对不确定性，做出明智的选择，从而实现更好的结果。随着数据在我们生活中发挥越来越重要的作用，掌握这些概念对于任何希望利用数据驱动洞察力的人来说都是无价的。