arima

问题 I am trying to implement a time series model and getting some strange exceptions that tells nothing to me. I wonder if I am making a mistake or if it is totally expected. Here comes details... When training my model, I try to make a grid search to find the best (p, d, q) settings. Here is the complete code (and I will explain down below what is happening here): The reproducible code below is essentially a copy from https://machinelearningmastery.com/grid-search-arima-hyperparameters-with

问题 I am trying to implement a time series model and getting some strange exceptions that tells nothing to me. I wonder if I am making a mistake or if it is totally expected. Here comes details... When training my model, I try to make a grid search to find the best (p, d, q) settings. Here is the complete code (and I will explain down below what is happening here): The reproducible code below is essentially a copy from https://machinelearningmastery.com/grid-search-arima-hyperparameters-with

来源： https://stackoverflow.com/questions/64339451/why-in-fable-forecast-libraries-residual-diagnostics-of-arima-models-is-done-wit

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

来源： https://stackoverflow.com/questions/63552562/how-to-count-how-many-times-an-arima-order-is-not-true-before-the-first-true-ord

问题 How do I check what the RMSE alone is in ARIMA(p,d,q) . If I simulate a time series data with arima.sim like this. wn <- rnorm(10, mean = 0, sd = 1) ar <- wn[1:2] for (i in 3:10){ ar<-arima.sim(n=10,model=list(ar=-0.7048,order=c(1,1,0)),start.innov=4.1,n.start=1,innov=wn) } ar <-ar[-1] I have tried the r code bellow: mis <- auto.arima(ar) summary(auto.arima(ar)) mod1 <- auto.arima(ar) refit <- Arima(ar, model=mod1) acu<-accuracy(refit) acu$ I want a function like rmse<-function(mis,...) that

问题 I want to simulate ARIMA(1,1,0) with varying: sample sizes phi values standard deviation values. I admire how the bellow r code is simulating just one ARIMA(1,1,0) which I want to follow the format to simulate many ARIMA(1,1,0) with varying sample sizes , phi values and standard deviation values wn <- rnorm(10, mean = 0, sd = 1) ar <- wn[1:2] for (i in 3:10){ ar<- arima.sim(n=10,model=list(ar=-0.7048,order=c(1,1,0)),start.innov=4.1,n.start=1,innov=wn) } I have asked a similar question here

问题 I want to simulate ARIMA(1,1,0) with varying: sample sizes phi values standard deviation values. I admire how the bellow r code is simulating just one ARIMA(1,1,0) which I want to follow the format to simulate many ARIMA(1,1,0) with varying sample sizes , phi values and standard deviation values wn <- rnorm(10, mean = 0, sd = 1) ar <- wn[1:2] for (i in 3:10){ ar<- arima.sim(n=10,model=list(ar=-0.7048,order=c(1,1,0)),start.innov=4.1,n.start=1,innov=wn) } I have asked a similar question here

问题 I have a time series at weekly level with seasonlity of 2 years. I am using auto_arima to forecast for next 2 years. Plot of the time series - The challenge is - its taking more than 90 mins to train the model with following parameters. stepwise_model = auto_arima(onion_data_train_weekly['Modal_Price'], start_p=1, start_q=1, max_p=1, max_q=1, m=104, start_P=0, seasonal=True, d=1, D=1, trace=True, error_action='ignore', suppress_warnings=True, stepwise=True) print(stepwise_model.aic()) Note:

问题 I have a time series at weekly level with seasonlity of 2 years. I am using auto_arima to forecast for next 2 years. Plot of the time series - The challenge is - its taking more than 90 mins to train the model with following parameters. stepwise_model = auto_arima(onion_data_train_weekly['Modal_Price'], start_p=1, start_q=1, max_p=1, max_q=1, m=104, start_P=0, seasonal=True, d=1, D=1, trace=True, error_action='ignore', suppress_warnings=True, stepwise=True) print(stepwise_model.aic()) Note: