arima

问题 I wish to run for loop in parallel process . The result I have with the for loop R code is good to my taste but will be applying it to a very huge data thus, the timing of the execution is slow. library(forecast) library(dplyr) arima_order_results = data.frame() seed_out2 <- c(1, 16, 170, 178, 411, 630, 661, 1242, 1625, 1901, 1926, 1927, 1928, 2170, 2779, 3687, 4139, 4583, 4825, 4828, 4829, 4827, 5103, 5211, 5509, 5561, 5569, 5679, 6344, 6490, 6943, 6944, 6945, 6946, 6948, 6950, 6951, 6952)

问题 This solution Automate Seed as a Vector Instead of an Integer in R library(forecast) SEED_vector <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) arima_order_results = data.frame() for (my_seed in SEED_vector){ set.seed(my_seed) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") arima_order = arimaorder(ar2) arima_order = t(as.data.frame(arima_order)) # Print the arima order. print(arima_order) # This line of code is just if you

问题 This solution Automate Seed as a Vector Instead of an Integer in R library(forecast) SEED_vector <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) arima_order_results = data.frame() for (my_seed in SEED_vector){ set.seed(my_seed) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") arima_order = arimaorder(ar2) arima_order = t(as.data.frame(arima_order)) # Print the arima order. print(arima_order) # This line of code is just if you

问题 This solution Automate Seed as a Vector Instead of an Integer in R library(forecast) SEED_vector <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) arima_order_results = data.frame() for (my_seed in SEED_vector){ set.seed(my_seed) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") arima_order = arimaorder(ar2) arima_order = t(as.data.frame(arima_order)) # Print the arima order. print(arima_order) # This line of code is just if you

问题 This solution Automate Seed as a Vector Instead of an Integer in R library(forecast) SEED_vector <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) arima_order_results = data.frame() for (my_seed in SEED_vector){ set.seed(my_seed) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") arima_order = arimaorder(ar2) arima_order = t(as.data.frame(arima_order)) # Print the arima order. print(arima_order) # This line of code is just if you

问题 I have financial data and my goal is to be able to forecast. I ran an arima model and found that the best fit was arima(1,1,1) w/ drift. I want to use GARCH on the data set because it is the better model to use due to volatility and when I squared my residuals it did have the arch effect. But I know that GARCH takes in a 2 parameter arima and I am not sure how that translates from the 3 parameter arima I currently have. library(dplyr) library(tidyr) library(lubridate) library(ggplot2) library

问题 I have financial data and my goal is to be able to forecast. I ran an arima model and found that the best fit was arima(1,1,1) w/ drift. I want to use GARCH on the data set because it is the better model to use due to volatility and when I squared my residuals it did have the arch effect. But I know that GARCH takes in a 2 parameter arima and I am not sure how that translates from the 3 parameter arima I currently have. library(dplyr) library(tidyr) library(lubridate) library(ggplot2) library

问题 Here is the algorithm of what I want to do with R : Simulate 10 time series data set from ARIMA model through arima.sim() function Split the series into sub-series of possible 2s , 3s , 4s , 5s , 6s , 7s , 8s , and 9s . For each size take a resample the blocks with replacement, for new series and obtain the best ARIMA model from the subseries from each block size through auto.arima() function. Obtain for each subseries of each block sizes RMSE . The below R function get that done. ## Load

问题 Here is the algorithm of what I want to do with R : Simulate 10 time series data set from ARIMA model through arima.sim() function Split the series into sub-series of possible 2s , 3s , 4s , 5s , 6s , 7s , 8s , and 9s . For each size take a resample the blocks with replacement, for new series and obtain the best ARIMA model from the subseries from each block size through auto.arima() function. Obtain for each subseries of each block sizes RMSE . The below R function get that done. ## Load

问题 Apparently, Anaconda has a different pyramid package and it is for web framework. https://anaconda.org/anaconda/pyramid The user guide for arima pyramid suggests that one uses pip to install pyramid-arima. https://www.alkaline-ml.com/pyramid/setup.html#setup But since Anaconda uses conda to configure packages, how do I add pyramid-arima to the Anaconda environment for Jupyter notebook? 回答1: pyramid-arima was recently renamed to pmdarima to avoid collisions with the Pyramid web framework. Try