一次特别的拜访
时间序列预测步骤
I am a strong believer in “learning by doing” philosophy.
我坚信“做中学”的哲学。
Data science is an applied field, so you need to get your feet wet to learn something. One can read all the “how to”
tutorials on swimming, but at some point, they do have to test the water.
数据科学是⼀个应⽤领域，因此您需要专⼼学习。 ⼈们可以阅读所有有关游泳的“⼊门指南”，但是在某些时候，他们确实必须测试⽔。
Beginners in data science often get caught into the impression that they have to learn everything under the sun before they
can do a project. Wrong! I believe people can learn faster not by reading stuff but by doing small bits and pieces of projects.
数据科学的初学者常常会陷⼊⼀种印象，即他们必须在阳光下学习⼀切，然后才能进⾏项⽬。 错误！ 我相信⼈们可以通过阅读⼀些零碎的
项⽬⽽不是阅读东西来更快地学习。
In this article I want you to learn how to fit a time series forecasting model ARIMA — which, for many, is an intimidating
algorithm. In this article, you will learn it in just 5 easy steps and make real forecasts. You are not going to build a Ferrari,
but I’m sure you will learn to build a car that you can take to the streets.
在本⽂中，我希望您学习如何拟合时间序列预测模型ARIMA-对许多⼈来说，这是⼀个令⼈⽣畏的算法。 在本⽂中，您将仅需5个简单的步
骤就可以学习它并做出真实的预测。 您不会制造法拉利，但是我敢肯定，您将学到制造可以上街的汽车。
Let’s roll the sleeves.
让我们卷起袖⼦。
步骤1：资料准备 (Step 1: Data preparation)
For this demo, we are going to use a forecasting package called fpp2 in R programming environment. Let’s load that
package.
对于此演⽰，我们将在R编程环境中使⽤称为fpp2的预测包。 让我们加载该程序包。
# Required packages
library(fpp2)
I’ve got some data that I extracted from an actual time series. The following are the values, let’s copy them in the R script
as well.
我有⼀些从实际时间序列中提取的数据。 以下是值，我们也将它们复制到R脚本中。
# your data
values = c(92.1, 92.6, 89.5, 80.9, 95.6, 72.5, 71.2, 78.8, 73.8, 83.5, 97.9, 93.4, 98.0, 90.2, 96.7, 100.0, 103.6, 74.6, 78.9, 92.0, 83.4, 98.1, 109.9, 102
Like every other modeling software, this package has a specific data formatting requirement. The ts() function takes care of
it by converting data into a time series object.
与其他所有建模软件⼀样，此软件包也有特定的数据格式要求。 ts()函数通过将数据转换为时间序列对象来进⾏处理。
In this function we specify the starting year (2015) and 12-month frequency.
在此函数中，我们指定开始年份(2015)和12个⽉的频率。
# your time series
time_series = ts(values, start = 2015, frequency =12)
腰医绅
步骤2：时间序列分解 (Step 2: Time series decomposition)
Decomposition basically means deconstructing and visualizing the series into its component parts.
分解基本上意味着将系列分解和可视化为其组成部分。
# time series decomposition
autoplot(decompose(time_series)) + theme(plot.title = element_text(size=8))
This figure below displays 4 pieces of information: your data (top one), overall trend and seasonality. The final piece is called the remainder or the random part.
下图显⽰了4条信息：您的数据(第⼀数据)，总体趋势和季节性。 最后的部分称为余数或随机部分。
Time series decomposition
时间序列分解
步骤3：建模 (Step 3: Modeling)
The actual model building is a simple 2-lines code using auto.arima() function. auto.arima will take care of the optimum parameter values, you just need to specify a few boolean parameters.
实际的模型构建是使⽤auto.arima()函数的简单两⾏代码。 auto.arima将照顾最佳参数值，您只需要指定⼀些布尔参数即可。
model = auto.arima(time_series, seasonal = TRUE, stepwise = FALSE, approximation = FALSE)
步骤4：预测 (Step 4: Forecasting)
Making an actual forecast is the simplest of all the steps above, just half of a line length code— can you believe? We are using forecast() function and passing the model above and specifying the number of time steps into the future you want to forecast (I specified 30 months ahead)
进⾏实际的预测是上述所有步骤中最简单的，只是⾏长代码的⼀半-您可以相信吗？ 我们正在使⽤forecast()函数并在上⾯传递模型，并指定了您要预测的未来时间步长(我指定了30个⽉)
# making forecast
forecast_arima = forecast(model, h=30)
You are practically done with forecasting. You can print the forecast values with the print(forecast_arima) function.
预测实际上已经完成。 您可以使⽤print(forecast_arima)函数打印预测值。
Forecast values
新金瓶梅2下载预测值
Or, you may want to visualize the forecast values, the input series and confidence intervals altogether.
或者，您可能希望完全可视化预测值，输⼊序列和置信区间。
# visualizing forecast
autoplot(time_series, series = " Data") +
autolayer(forecast_arima, series = "Forecast") +
ggtitle(" Forecasting with ARIMA") +
theme(plot.title = element_text(size=8))
Image for post
Forecasting with ARIMA
⽤ARIMA进⾏预测
步骤5：模型评估 (Step 5: Model evaluation)
This is an extra step for model evaluation and accuracy tests. First, let’s check out model description:
这是模型评估和准确性测试的额外步骤。 ⾸先，让我们检查⼀下模型描述：
# model description
model['model']
I highlighted few things that you might be interested in: the description of the model (ARIMA(0,1,2(0,1,1)[12]) and AIC values. AIC is often used to compare the performance of two or more models.
我重点介绍了您可能感兴趣的⼏件事：模型的描述(ARIMA(0,1,2(0,1,1)[12])和AIC值。AIC通常⽤于⽐较两个或两个的性能更多型号。
Image for post
In most machine learning models accuracy is determined based on RMSE or MAE values. Let’s print them as well.
在⼤多数机器学习模型中，准确性是基于RMSE或MAE值确定的。 让我们也打印它们。
# accuracy
accuracy(model)
Image for post
That is all!
就这些！
下⼀步 (Next steps)
You have just built and implemented a forecasting model using 5 simple steps. Does that mean you became a master of forecasting? No, but you know the overall structure of the model from beginning to end and able to play with it with different datasets, different parameter values etc.
您刚刚使⽤5个简单的步骤构建并实施了预测模型。 这是否意味着您成为预测⼤师？ 不，但是您从头到尾都知道模型的整体结构，并且能够与不同的数据集，不同的参数值等⼀起使⽤。
Just like I said in the beginning, you haven’t built a Ferrari but you’ve built a car that you can take to the grocery store!
就像我在开始时说的那样，您还没有制造法拉利，但是您已经制造了可以带到杂货店的汽车！
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时间序列预测步骤
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