seq生成序列-f参数
时间序列-参数校准 (Time Series - Parameter Calibration)
介绍 (Introduction)
Any statistical or machine learning model has some parameters which greatly influence how the data is modeled. For example, ARIMA has p, d, q values. These parameters are to be decided such that the error between actual values and modeled values is minimum. Parameter calibration is said to be the most crucial and time-consuming task of model fitting. Hence, it is very essential for us to choose optimal parameters.
任何统计或机器学习模型都具有一些参数，这些参数会极大地影响数据建模的方式。 例如，ARIMA具有p，d，q值。 确定这些参数，以使实际值和建模值之间的误差最小。 据说参数校准是模型拟合的最关键和最耗时的任务。 因此，选择最优参数对我们来说至关重要。
参数校准方法 (Methods for Calibration of Parameters)
There are various ways to calibrate parameters. This section talks about some of them in detail.
有多种校准参数的方法。 本节详细讨论其中的一些。
尝试 (Hit-and-try)
One common way of calibrating models is hand calibration, where you start by visualizing the time-series and intuitively try some parameter values and change them over and over until you achieve a good enough fit. It requires a good understanding of the model we are trying. For ARIMA model, hand calibration is done with the help of auto-correlation plot for ‘p’ parameter, partial auto-correlation plot for ‘q’ parameter and ADF-test to confirm the stationarity of time-series and setting ‘d’ parameter. We will discuss all these in detail in the coming chapters.
校准模型的一种常见方法是手动校准，从可视化时间序列开始，然后直观地尝试一些参数值，并不断地更改它们，直到达到足够好的拟合度为止。 它需要对我们正在尝试的模型有很好的了解。 对于ARIMA模型，借助“ p”参数的自相关图，“ q”参数的部分自相关图和ADF测试进行手动校准，以确认时间序列的平稳性并设置“ d”参数。 在接下来的章节中，我们将详细讨论所有这些内容。
网格搜索 (Grid Search)
Another way of calibrating models is by grid search, which essentially means you try building a model for all possible combinations of parameters and select the one with minimum error. This is time-consuming and hence is useful when number of parameters to be calibrated and range of values they take are fewer as this involves multiple nested for loops.
校准模型的另一种方法是通过网格搜索，这实际上意味着您尝试为所有可能的参数组合构建模型并选择误差最小的模型。 这很耗时，因此在要校准的参数数量和所取值范围较小时很有用，因为这涉及多个嵌套的for循环。
遗传算法 (Genetic Algorithm)
Genetic algorithm works on the biological principle that a good solution will eventually evolve to the most ‘optimal’ solution. It uses biological operations of mutation, cross-over and selection to finally reach to an optimal solution.
遗传算法基于生物学原理，即良好的解决方案最终将演变为最“最佳”的解决方案。 它使用突变，交叉和选择的生物学操作最终达到最佳解决方案。
For further knowledge you can read about other parameter optimization techniques like Bayesian optimization and Swarm optimization.
有关更多的知识，您可以阅读有关其他参数优化技术的信息，例如贝叶斯优化和Swarm优化。
翻译自: https://www.tutorialspoint.com/time_series/time_series_parameter_calibration.htm
seq生成序列-f参数