如何在预测中指定最小或最大可能值?

这个更具体，我没有讨论. 数据需要对数转换但在某处可能需要0时该怎么办.我只是添加一个小的容限，例如说yy <- log(y + 1e-7)继续.Is there a way to specify minimum or maximum possible values in a forecast done with ETS/ARIMA models?Such as when forecasting a trend in % that can only go between 0% and 100%.I am using R package forecast (and function forecast). 解决方案 If your time series y has a natural bound [a, b], you should take a "logit-alike" transform first:f <- function (x, a, b) log((x - a) / (b - x))yy <- f(y, a, b)Then the resulting yy is unbounded on (-Inf, Inf), suitable for Gaussian error assumption. Use yy for time series modelling, and take back-transform later on the prediction / forecast:finv <- function (x, a, b) (b * exp(x) + a) / (exp(x) + 1)y <- finv(yy, a, b)Note, the above transform f (hence finv) is monotone, so if the 95%-confidence interval for yy is [l, u], the corresponding confidence interval for y is [finv(l), finv(u)].If your y is only bounded on one side, consider "log-alike" transform.bounded on [a, Inf), consider yy <- log(y - a);bounded on (-Inf, a], consider yy <- log(a - y).Wow, I didn't know Rob Hyndman has a blog. Thanks to @ulfelder for providing it. I added it here to make my answer more solid: Forecasting within limits.This one is more specific, which I have not covered. What to do when data need a log transform but it can take 0 somewhere. I would just add a small tolerance, say yy <- log(y + 1e-7) to proceed. 这篇关于如何在预测中指定最小或最大可能值?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！