forecasting

来源： https://stackoverflow.com/questions/37400062/seasonality-in-auto-arima-from-forecast-package

来源： https://stackoverflow.com/questions/63331366/r-time-series-auto-arima-xreg-dimension-is-zero-error

来源： https://stackoverflow.com/questions/41571215/forecasr-accuracy-mape-and-zero-values

问题 I am trying to forecast a time series object in R with GARCH(1,1) model. My goal is to hav 24 instances ahead forecast with the GARCH model. Although I am using a time series object while forecasting,I get the following error: Error in is.constant(y) : (list) object cannot be coerced to type 'double' Those are the commands that I am using: library(forecast) library(tseries) trainer1 <- ts(trainer, frequency=24) m1 <- garch(trainer1, order = c(1,1)) forecasts1 <- forecast(m1, h=24) And the

问题 I am trying to forecast a time series object in R with GARCH(1,1) model. My goal is to hav 24 instances ahead forecast with the GARCH model. Although I am using a time series object while forecasting,I get the following error: Error in is.constant(y) : (list) object cannot be coerced to type 'double' Those are the commands that I am using: library(forecast) library(tseries) trainer1 <- ts(trainer, frequency=24) m1 <- garch(trainer1, order = c(1,1)) forecasts1 <- forecast(m1, h=24) And the

问题 I'd like to create a new table in Google Big Query with existing daily revenue data and extend this new table with forecast data which is based on the existing data and needs to be created. Once new actual data exists for a certain day it overrides the forecast data for that day. Also, the forecast data until the end of the month is then updated again. So far, I came up with the following, which generates an error message : Scalar subquery produced more than one element SELECT date, sum(yl

问题 I'm not sure if this is intended behaviour. Consider the following snippet of code - library(forecast) x <- c( 0, 0, 0, 0, 0.00217764964493354, 0.00339032724317772, 0.00357374918778428, 0.00282328811130057, 0.00272679331678393, 0.0030360769697858, 0.00316665914235777, 0.00163300219677676, 0.00249817841157489, 0.00207838479809976, 0.00192104504850639, 0.00209700948212983, 0.00216356555603635, 0.00250983016815862, 0.0017474879860201 ) tsData <- ts(data = x, start = 2000, frequency = 1) df <-

问题 I'm not sure if this is intended behaviour. Consider the following snippet of code - library(forecast) x <- c( 0, 0, 0, 0, 0.00217764964493354, 0.00339032724317772, 0.00357374918778428, 0.00282328811130057, 0.00272679331678393, 0.0030360769697858, 0.00316665914235777, 0.00163300219677676, 0.00249817841157489, 0.00207838479809976, 0.00192104504850639, 0.00209700948212983, 0.00216356555603635, 0.00250983016815862, 0.0017474879860201 ) tsData <- ts(data = x, start = 2000, frequency = 1) df <-

问题 I have signals recorded from machines (m1, m2, so on) for 28 days. (Note: each signal in each day is 360 length long). machine_num, day1, day2, ..., day28 m1, [12, 10, 5, 6, ...], [78, 85, 32, 12, ...], ..., [12, 12, 12, 12, ...] m2, [2, 0, 5, 6, ...], [8, 5, 32, 12, ...], ..., [1, 1, 12, 12, ...] ... m2000, [1, 1, 5, 6, ...], [79, 86, 3, 1, ...], ..., [1, 1, 12, 12, ...] I want to predict the signal sequence of each machine for next 3 days. i.e. in day29 , day30 , day31 . However, I don't

问题 I have a .csv file containing a 5-year time series, with hourly resolution (commoditiy price). Based on the historical data, I want to create a forecast of the prices for the 6th year. I have read a couple of articles on the www about these type of procedures, and I basically based my code on the code posted there, since my knowledge in both Python (especially statsmodels) and statistic is at most limited. Those are the links, for those who are interested: http://www.seanabu.com/2016/03/22