发生预测 [英] Occurrence prediction
问题描述
我想知道哪种方法最适合预测事件发生.例如，给定一组来自 5 年疟疾感染事件的数据以及影响这些事件的其他几个因素，我想预测未来五年的疟疾感染事件.我的想法是用模糊逻辑规则推导出一种出现因子，然后用出现因子对出现的次数进行平均得到第一个预测的出现次数，然后再用预测的出现次数对所有出现的次数进行平均，并继续对所有五个进行迭代年，但我决定在线寻求帮助.
I'd like to know what method is best suited for predicting event occurrences. For example, given a set of data from 5 years of malaria infection occurrences and several other factors that affect the occurrences, I'd like to predict the next five years for malaria infection occurrences. What I thought of doing was to derive a kind of occurrence factor using fuzzy logic rules, and then average the occurrences with the occurrence factor to get the first predicted occurrence, and then average all again with the predicted occurrence and keep on iterating for all five years, but I decided to seek for help online.
推荐答案
进行预测的方法有很多种，每种方法都有自己的优点和缺点.确定预测准确性的科学通常包括尽量减少错误.所有预测都归结为使用过去作为未来的预测指标，并对其进行一定程度的调整.例如.明天的温度会和今天一样，加减一些.您决定 +/- 的方式因人而异.
There are many ways to do forecasting, each has its own advantages and disadvantages. The science of determining the accuracy of a forecast often consists of trying to minimize error. All forecasting comes down to using the past as a predictor of the future, adjusting it by some amount. E.g. tomorrow the temperature will be the same as today, plus or minus some amount. How you decide the +/- is what varies.
以下是您可能想要查看的一系列技术:
Here are a range of techniques you might want to review:
- 移动平均线(简单、单一、双重)
- 指数平滑
- 分解(趋势+季节性+周期性+不规则性)
- 线性回归
- 多重回归
- Box-Jenkis(又名 ARIMA，自回归综合运动平均)
抱歉，答案含糊不清，但预测是一件复杂的事情.
Sorry, for the vague answer but forecasting is complex stuff.
您所描述的将您的预测反馈回模型以生成未来预测的内容是标准的.我不知道模糊逻辑"是否让你有什么特别的地方.正如任何预测讲师都会告诉您的那样，有时您只是眯着眼睛看数据.上下文就是一切.
What you describe about feeding your predictions back into the model to produce future predictions is standard stuff. I don't know if "fuzzy logic" gets you anything in particular. As any forecasting instructor will tell you, sometimes you just squint and look at the data. Context is everything.
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