Auto-Arima creates a straight line help





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I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
This is what i've got so far



install.packages("forecast")
install.packages("scales")
library(forecast)
datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
monto=datos$monto.XVI
montots<-ts(monto)
montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
montots
plot(montots)
auto.arima(montots)
fit=arima(montots,order=c(0,1,0))
a=forecast(fit,h=5)
plot(forecast(fit,h=5))


So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
enter image description here



my data looks like thisenter image description here



thank you










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sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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    up vote
    2
    down vote

    favorite
    1












    I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
    This is what i've got so far



    install.packages("forecast")
    install.packages("scales")
    library(forecast)
    datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
    monto=datos$monto.XVI
    montots<-ts(monto)
    montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
    montots
    plot(montots)
    auto.arima(montots)
    fit=arima(montots,order=c(0,1,0))
    a=forecast(fit,h=5)
    plot(forecast(fit,h=5))


    So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
    enter image description here



    my data looks like thisenter image description here



    thank you










    share|cite|improve this question







    New contributor




    sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.






















      up vote
      2
      down vote

      favorite
      1









      up vote
      2
      down vote

      favorite
      1






      1





      I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
      This is what i've got so far



      install.packages("forecast")
      install.packages("scales")
      library(forecast)
      datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
      monto=datos$monto.XVI
      montots<-ts(monto)
      montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
      montots
      plot(montots)
      auto.arima(montots)
      fit=arima(montots,order=c(0,1,0))
      a=forecast(fit,h=5)
      plot(forecast(fit,h=5))


      So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
      enter image description here



      my data looks like thisenter image description here



      thank you










      share|cite|improve this question







      New contributor




      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
      This is what i've got so far



      install.packages("forecast")
      install.packages("scales")
      library(forecast)
      datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
      monto=datos$monto.XVI
      montots<-ts(monto)
      montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
      montots
      plot(montots)
      auto.arima(montots)
      fit=arima(montots,order=c(0,1,0))
      a=forecast(fit,h=5)
      plot(forecast(fit,h=5))


      So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
      enter image description here



      my data looks like thisenter image description here



      thank you







      r time-series forecasting arima prediction






      share|cite|improve this question







      New contributor




      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|cite|improve this question







      New contributor




      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|cite|improve this question




      share|cite|improve this question






      New contributor




      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked Nov 19 at 6:37









      sarah lopez

      111




      111




      New contributor




      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.






















          1 Answer
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          oldest

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          up vote
          6
          down vote













          Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.



          By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:



          $$ y_t-y_{t-1} = epsilon_t, $$



          or



          $$ y_t=y_{t-1} + epsilon_t. $$



          That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.



          For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:



          $$ hat{y}_t=y_{t-1}. $$



          And this is iterated. You end up with a flat line.



          Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?



          You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.






          share|cite|improve this answer





















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            active

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes








            up vote
            6
            down vote













            Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.



            By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:



            $$ y_t-y_{t-1} = epsilon_t, $$



            or



            $$ y_t=y_{t-1} + epsilon_t. $$



            That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.



            For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:



            $$ hat{y}_t=y_{t-1}. $$



            And this is iterated. You end up with a flat line.



            Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?



            You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.






            share|cite|improve this answer

























              up vote
              6
              down vote













              Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.



              By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:



              $$ y_t-y_{t-1} = epsilon_t, $$



              or



              $$ y_t=y_{t-1} + epsilon_t. $$



              That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.



              For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:



              $$ hat{y}_t=y_{t-1}. $$



              And this is iterated. You end up with a flat line.



              Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?



              You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.






              share|cite|improve this answer























                up vote
                6
                down vote










                up vote
                6
                down vote









                Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.



                By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:



                $$ y_t-y_{t-1} = epsilon_t, $$



                or



                $$ y_t=y_{t-1} + epsilon_t. $$



                That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.



                For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:



                $$ hat{y}_t=y_{t-1}. $$



                And this is iterated. You end up with a flat line.



                Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?



                You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.






                share|cite|improve this answer












                Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.



                By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:



                $$ y_t-y_{t-1} = epsilon_t, $$



                or



                $$ y_t=y_{t-1} + epsilon_t. $$



                That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.



                For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:



                $$ hat{y}_t=y_{t-1}. $$



                And this is iterated. You end up with a flat line.



                Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?



                You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Nov 19 at 7:08









                Stephan Kolassa

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