Dear all, I am trying to construct a fan chart (interval forecasts) for out-of-sample forecasts for 12 months (2022m6-2023m5) after ARIMA model. The data is the monthly sales (sales) from 2004 m7 to 2022 m5. The command I used is
arima sales, arima(2,1,8) after looking into the partial autocorrelation and autocorrelation of sales. I would be thankful for your suggestions.
arima sales, arima(2,1,8) after looking into the partial autocorrelation and autocorrelation of sales. I would be thankful for your suggestions.
t | sales |
2004m7 | 2 |
2004m8 | 2.4 |
2004m9 | 2.6 |
2004m10 | 2.6 |
2004m11 | 2.7 |
2004m12 | 3.1 |
2005m1 | 4.6 |
2005m2 | 5.7 |
2005m3 | 5.7 |
2005m4 | 5.8 |
2005m5 | 6.4 |
2005m6 | 6.2 |
2005m7 | 6.6 |
2005m8 | 7.3 |
2005m9 | 8.2 |
2005m10 | 7.8 |
2005m11 | 8.5 |
2005m12 | 8.8 |
2006m1 | 7 |
2006m2 | 5.8 |
2006m3 | 7.7 |
2006m4 | 7.9 |
2006m5 | 9.1 |
2006m6 | 9.1 |
2006m7 | 8.3 |
2006m8 | 4.5 |
2006m9 | 5.5 |
2006m10 | 6.5 |
2006m11 | 6.4 |
2006m12 | 5.8 |
2007m1 | 4.6 |
2007m2 | 5.1 |
2007m3 | 5.4 |
2007m4 | 5.6 |
2007m5 | 6.6 |
2007m6 | 6.7 |
2007m7 | 8 |
2007m8 | 5.6 |
2007m9 | 6.1 |
2007m10 | 5.4 |
2007m11 | 5.3 |
2007m12 | 4.6 |
2008m1 | 4.8 |
2008m2 | 5.2 |
2008m3 | 6 |
2008m4 | 8 |
2008m5 | 8.3 |
2008m6 | 10.1 |
2008m7 | 10.6 |
2008m8 | 11.8 |
2008m9 | 12.4 |
2008m10 | 13.2 |
2008m11 | 13.7 |
2008m12 | 13.4 |
2009m1 | 13.8 |
2009m2 | 13.2 |
2009m3 | 12.9 |
2009m4 | 11.6 |
2009m5 | 12.4 |
2009m6 | 12 |
2009m7 | 11.1 |
2009m8 | 10.1 |
2009m9 | 9.1 |
2009m10 | 8.5 |
2009m11 | 9.1 |
2009m12 | 10.2 |
2010m1 | 10.7 |
2010m2 | 10.9 |
2010m3 | 9.9 |
2010m4 | 9.7 |
2010m5 | 8.8 |
2010m6 | 8.2 |
2010m7 | 9 |
2010m8 | 9.5 |
2010m9 | 8.6 |
2010m10 | 8.9 |
2010m11 | 8.4 |
2010m12 | 9.6 |
2011m1 | 11.3 |
2011m2 | 10.2 |
2011m3 | 10.7 |
2011m4 | 10.6 |
2011m5 | 9.5 |
2011m6 | 8.8 |
2011m7 | 9.6 |
2011m8 | 7.7 |
2011m9 | 8.5 |
2011m10 | 8.9 |
2011m11 | 8.4 |
2011m12 | 7.5 |
2012m1 | 6.8 |
2012m2 | 7.1 |
2012m3 | 7 |
2012m4 | 7.5 |
2012m5 | 8.7 |
2012m6 | 9.9 |
2012m7 | 11.5 |
2012m8 | 11.9 |
2012m9 | 11.2 |
2012m10 | 10.5 |
2012m11 | 10.5 |
2012m12 | 10.4 |
2013m1 | 9.8 |
2013m2 | 10.1 |
2013m3 | 10.2 |
2013m4 | 9.5 |
2013m5 | 8.7 |
2013m6 | 8.2 |
2013m7 | 7.8 |
2013m8 | 7.9 |
2013m9 | 8 |
2013m10 | 8.4 |
2013m11 | 10 |
2013m12 | 10.3 |
2014m1 | 9.7 |
2014m2 | 8.8 |
2014m3 | 8.9 |
2014m4 | 9.4 |
2014m5 | 9.7 |
2014m6 | 9.5 |
2014m7 | 8.1 |
2014m8 | 7.5 |
2014m9 | 7.6 |
2014m10 | 7.5 |
2014m11 | 7.2 |
2014m12 | 7 |
2015m1 | 6.8 |
2015m2 | 7 |
2015m3 | 7 |
2015m4 | 6.9 |
2015m5 | 7.1 |
2015m6 | 7.4 |
2015m7 | 7.6 |
2015m8 | 6.9 |
2015m9 | 7.2 |
2015m10 | 8.2 |
2015m11 | 10.4 |
2015m12 | 11.6 |
2016m1 | 12.1 |
2016m2 | 11.3 |
2016m3 | 10.2 |
2016m4 | 9.7 |
2016m5 | 10 |
2016m6 | 11.1 |
2016m7 | 10.4 |
2016m8 | 8.6 |
2016m9 | 7.9 |
2016m10 | 6.7 |
2016m11 | 4.8 |
2016m12 | 3.8 |
2017m1 | 3.2 |
2017m2 | 3.3 |
2017m3 | 2.9 |
2017m4 | 3.8 |
2017m5 | 3.4 |
2017m6 | 2.8 |
2017m7 | 2.7 |
2017m8 | 2.3 |
2017m9 | 3.4 |
2017m10 | 3.1 |
2017m11 | 3.9 |
2017m12 | 4.2 |
2018m1 | 4 |
2018m2 | 5 |
2018m3 | 6 |
2018m4 | 5.3 |
2018m5 | 4.1 |
2018m6 | 4.1 |
2018m7 | 4.6 |
2018m8 | 4.2 |
2018m9 | 3.9 |
2018m10 | 4.7 |
2018m11 | 4.2 |
2018m12 | 3.7 |
2019m1 | 4.6 |
2019m2 | 4.4 |
2019m3 | 4.2 |
2019m4 | 4.4 |
2019m5 | 5.3 |
2019m6 | 6.2 |
2019m7 | 6 |
2019m8 | 7 |
2019m9 | 6.2 |
2019m10 | 6.2 |
2019m11 | 5.8 |
2019m12 | 6.6 |
2020m1 | 6.8 |
2020m2 | 6.9 |
2020m3 | 6.7 |
2020m4 | 6.7 |
2020m5 | 5.8 |
2020m6 | 4.5 |
2020m7 | 4.8 |
2020m8 | 3.5 |
2020m9 | 4.5 |
2020m10 | 3.8 |
2020m11 | 4.1 |
2020m12 | 2.9 |
2021m1 | 3.6 |
2021m2 | 2.7 |
2021m3 | 3 |
2021m4 | 3.1 |
2021m5 | 3.7 |
2021m6 | 4.2 |
2021m7 | 4.2 |
2021m8 | 4.35 |
2021m9 | 3.49 |
2021m10 | 4.24 |
2021m11 | 5.32 |
2021m12 | 7.11 |
2022m1 | 5.65 |
2022m2 | 5.97 |
2022m3 | 7.14 |
2022m4 | 7.28 |
2022m5 | 7.87 |