forecast

问题 I am using the auto.arima from the forecast package in R to determine the optimal K-terms for fourier series. After I do that, I want to then calculate the seasonality and plug that one seasonality variable into a multiple regression model. Using the dataset from the forecast package, I was able to extract the optimal amount of fourier terms: library(forecast) ##Public dataset from the forecast package head(gas) ##Choose Optimal Amount of K-Terms bestfit <- list(aicc=Inf) for(i in 1:6) { fit

问题 I am foresting with combination of data sets from fpp2 package and forecasting function from the forecast package. Output from this forecasting is object list with SNAIVE_MODELS_ALL. This object contain data separate for two series, where first is Electricity and second is Cement. You can see code below : # CODE library(fpp2) library(dplyr) library(forecast) library(gridExtra) library(ggplot2) #INPUT DATA mydata_qauselec <- qauselec mydata_qcement <- window(qcement, start = 1956, end = c(2010

问题 I have this data which is residual series obtained from predicted values and observations. original series was a random walk with a very small drift(mean=0.0025). err <- ts(c(0.6100, 1.3500, 1.0300, 0.9600, 1.1100, 0.8350 , 0.8800 , 1.0600 , 1.3800 , 1.6200, 1.5800 , 1.2800 , 1.3000 , 1.4300 , 2.1500 , 1.9100 , 1.8300 , 1.9500 ,1.9999, 1.8500 , 1.5500 , 1.9800 ,1.7044 ,1.8593 , 1.9900 , 2.0400, 1.8950, 2.0100 , 1.6900 , 2.1800 ,2.2150, 2.1293 , 2.1000 , 2.1200 , 2.0500 , 1.9000, 1.8350, 1

问题 I'm trying to find a way to apply monthly percentage changes to forecast pricing. I set my problem up in excel to make it a bit more clear. I'm using SQL Server 2017. We'll say all months before 9/1/18 are historical and 9/1/18 and beyond are forecasts. I need to calculate the forecast price (shaded in yellow on the sample data) using... Forecast Price = (Previous Row Forecast Price * Pct Change) + Previous Row Forecast Price Just to be clear, the yellow shaded prices do not exist in my data

问题 This question derived from this question Join residual by group in summary Forecast table in R where forecast performed using ets function, for each group(1 and 2) The only one and serious difference is that it works with one variable. if I have a lot of variables, i must perform forecast for all of them at once. Let's take example df=structure(list(Variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("x", "y"),