置信区间估计 预测区间估计
Estimation implies finding the optimal parameter using historical data whereas prediction uses the data to compute the random value of the unseen data.
估计意味着使用历史数据找到最佳参数，而预测则使用该数据来计算未见数据的随机值 。
The highlighted words in the above statement need some context setting before we proceed further:
在继续进行之前，上述语句中突出显示的词需要进行一些上下文设置：
We need lot of historical data to learn dependencies for machine learning and modelling. The data typically involves multiple observations, where each observation consists of multiple variables. This multivariate observation x belongs to random variable X whose distribution lies in the realm of a finite set of possible distributions called as ‘the states of nature’.
我们需要大量的历史数据来学习机器学习和建模的依赖关系。 数据通常包含多个观察值，其中每个观察值都包含多个变量。 该多元观测值x属于随机变量X，其分布位于称为“自然状态”的有限分布的可能范围内。
Estimation is the process of optimizing the true state of nature. Loosely speaking, estimation is related to model building i.e. finding the most appropriate parameter that best describes the multivariate distribution of historical data, for e.g. if we have five independent variables, X1, X2….X5 and Y as the target variable. Then, estimation involves the process of finding f(x) which is the closest approximation of the true state of nature denoted by g(θ).
估计是优化自然真实状态的过程 。 宽松地说，估计与模型构建有关，即找到最能描述历史数据多元分布的最合适参数，例如，如果我们有五个独立变量X1，X2….X5和Y作为目标变量。 然后，估计涉及寻找f(x)的过程，f(x)是由g(θ)表示的真实自然状态的最近似值。
Parameter estimation on training data 训练数据的参数估计Whereas, prediction leverages the already built model to compute the out of sample values. It is a process of calculating the value of another random variable Z whose distribution is related to the true state of the nature (this property plays a pivotal role in any machine learning algorithm). Predictions are considered good when they agree over all the possible values of Z, on an average.
而预测则利用已经建立的模型来计算样本外值。 这是计算另一个随机变量Z的值的过程，该变量的分布与自然的真实状态有关(此属性在任何机器学习算法中都起着关键作用)。 平均而言，当预测与Z的所有可能值一致时，这些预测就被认为是好的。
Prediction on unseen data 对看不见的数据进行预测There are multiple ways to interpret the difference between the two, let’s also explore the Bayesian intuition:
解释两者之间差异的方法有多种，让我们还探讨贝叶斯直觉 ：
Estimation is after the occurrence of the event i.e. posterior probability. Prediction is a kind of estimation before the occurrence of the event i.e. apriori probability.
估计是在事件发生之后，即后验概率。 预测是在事件发生之前进行的一种估计，即先验概率。
Let’s summarize our understanding on estimation and prediction: To make predictions on unseen data, we fit a model on training dataset that learns an estimator f(x), which is used to make predictions on new data.
让我们总结一下对估计和预测的理解：为了对看不见的数据进行预测，我们在训练数据集上拟合了一个模型，该模型学习了估计器f(x)，该函数用于对新数据进行预测。
Now, that we understand what the prediction is, let’s see how it is different from forecasting.
现在，我们了解了预测是什么，让我们看看它与预测有何不同。
Forecasting problems are a subset of prediction problems wherein both use the historical data and talk about the future events. The only difference between forecasting and prediction is the explicit addition of temporal dimension in forecasting.
预测问题是预测问题的子集，其中既使用历史数据，又谈论未来事件。 预测与预测之间的唯一区别是在预测中显式增加了时间维度。
Forecast is a time-based prediction i.e. it is more appropriate while dealing with time series data. Prediction, on the other hand, need not be time based only, it can be based on multiple causal factors that influence the target variable.
预测是基于时间的预测，即在处理时间序列数据时更合适。 另一方面，预测不必仅基于时间，它可以基于影响目标变量的多个因果因素。
I stumbled across a very fresh perspective of explaining the difference between the prediction and forecast using the analogy of the origin of the words themselves.
我偶然发现了一个非常新颖的观点，即使用单词本身的起源来解释预测与预测之间的差异。
I will brief on this innovative illustration in this post, but you can read more about it at the original post here.
我将在这篇文章中简要介绍这个创新的插图，但是您可以在此处的原始文章中了解更多有关它的信息。
Forecast is more process-oriented and follows a certain methodology of doing something. In a way, it assumes that the past behavior is a good enough indicator of what is going to happen in the future.
预测更注重过程，并遵循某种方法进行工作。 在某种程度上，它假设过去的行为足以说明将来会发生什么。
Prediction considers all historical processes, influencing variables and interactions to reveal the future.
预测考虑了所有历史过程，影响变量和相互作用以揭示未来。
In summary, all forecasts are predictions but not all predictions are forecasts.
总之，所有预测都是预测，但并非所有预测都是预测。
Hope you now have clarity on the difference between estimation and prediction. The post also highlights the distinction between prediction vs forecast.
希望您现在对估计和预测之间的区别有所了解。 该帖子还强调了预测与预测之间的区别。
Happy Reading!!!
阅读愉快！
References: https://stats.stackexchange.com/questions/17773/what-is-the-difference-between-estimation-and-prediction/17789#17789
参考： https : //stats.stackexchange.com/questions/17773/what-is-the-difference-between-estimation-and-prediction/17789#17789
翻译自: https://towardsdatascience.com/estimation-prediction-and-forecasting-40c56a5be0c9
置信区间估计 预测区间估计