I've done some research and I am stuck in finding the solution. I have a time series data, very basic data frame, let's call it x
:
Date Used
11/1/2011 587
11/2/2011 578
11/3/2011 600
11/4/2011 599
11/5/2011 678
11/6/2011 555
11/7/2011 650
11/8/2011 700
11/9/2011 600
11/10/2011 550
11/11/2011 600
11/12/2011 610
11/13/2011 590
11/14/2011 595
11/15/2011 601
11/16/2011 700
11/17/2011 650
11/18/2011 620
11/19/2011 645
11/20/2011 650
11/21/2011 639
11/22/2011 620
11/23/2011 600
11/24/2011 550
11/25/2011 600
11/26/2011 610
11/27/2011 590
11/28/2011 595
11/29/2011 601
11/30/2011 700
12/1/2011 650
12/2/2011 620
12/3/2011 645
12/4/2011 650
12/5/2011 639
12/6/2011 620
12/7/2011 600
12/8/2011 550
12/9/2011 600
12/10/2011 610
12/11/2011 590
12/12/2011 595
12/13/2011 601
12/14/2011 700
12/15/2011 650
12/16/2011 620
12/17/2011 645
12/18/2011 650
12/19/2011 639
12/20/2011 620
12/21/2011 600
12/22/2011 550
12/23/2011 600
12/24/2011 610
12/25/2011 590
12/26/2011 750
12/27/2011 750
12/28/2011 666
12/29/2011 678
12/30/2011 800
12/31/2011 750
I really appreciate any help with this. I am working with time series data and need to be able to create forecast based on historical data.
First I tried to convert it to xts
:
x.xts <- xts(x$Used, x$Date)
Then, I converted x.xts
to regular time series:
x.ts <- as.ts(x.xts)
Put the values in ets
:
x.ets <- ets(x.ts)
Performed forecasting for 10 periods:
x.fore <- forecast(x.ets, h=10)
x.fore
is this:
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
87 932.9199 831.7766 1034.063 778.2346 1087.605
88 932.9199 818.1745 1047.665 757.4319 1108.408
89 932.9199 805.9985 1059.841 738.8103 1127.029
90 932.9199 794.8706 1070.969 721.7918 1144.048
91 932.9199 784.5550 1081.285 706.0153 1159.824
92 932.9199 774.8922 1090.948 691.2375 1174.602
93 932.9199 765.7692 1100.071 677.2849 1188.555
94 932.9199 757.1017 1108.738 664.0292 1201.811
95 932.9199 748.8254 1117.014 651.3717 1214.468
96 932.9199 740.8897 1124.950 639.2351 1226.605
When I try to plot the x.fore
, I get a graph but the x-axis is showing numbers rather than dates:
Are the steps I am doing correct? How can I change the x-axis to read show dates?
I thank you so much for any input.
Here's what I did:
x$Date = as.Date(x$Date,format="%m/%d/%Y")
x = xts(x=x$Used, order.by=x$Date)
# To get the start date (305)
# > as.POSIXlt(x = "2011-11-01", origin="2011-11-01")$yday
## [1] 304
# Add one since that starts at "0"
x.ts = ts(x, freq=365, start=c(2011, 305))
plot(forecast(ets(x.ts), 10))
Resulting in:
What can we learn from this:
2011.85
means "day number 365*.85
" (day 310 in the year).as.POSIXlt(x = "2011-11-01", origin="2011-11-01")$yday
and figuring out the date from a day number can be done by using something like as.Date(310, origin="2011-01-01")
You can drop even more intermediate steps, since there's no reason to first convert your data into an xts.
x = ts(x$Used, start=c(2011, as.POSIXlt("2011-11-01")$yday+1), frequency=365)
# NOTE: We have only selected the "Used" variable
# since ts will take care of dates
plot(forecast(ets(x), 10))
This gives exactly the same result as the image above.
Building on the solution provided by @joran, you can try:
# 'start' calculation = `as.Date("2011-11-01")-as.Date("2011-01-01")+1`
# No need to convert anything to dates at this point using xts
x = ts(x$Used, start=c(2011, 305), frequency=365)
# Directly plot your forecast without your axes
plot(forecast(ets(x), 10), axes = FALSE)
# Generate labels for your x-axis
a = seq(as.Date("2011-11-01"), by="weeks", length=11)
# Plot your axes.
# `at` is an approximation--there's probably a better way to do this,
# but the logic is approximately 365.25 days in a year, and an origin
# date in R of `January 1, 1970`
axis(1, at = as.numeric(a)/365.25+1970, labels = a, cex.axis=0.6)
axis(2, cex.axis=0.6)
Which will yield:
Part of the problem in your original code is that after you have converted your data to an xts
object, and converted that to a ts
object, you lose the dates in your forecast
points.
Compare the first column (Point
) of your x.fore
output to the following:
> forecast(ets(x), 10)
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
2012.000 741.6437 681.7991 801.4884 650.1192 833.1682
2012.003 741.6437 676.1250 807.1624 641.4415 841.8459
2012.005 741.6437 670.9047 812.3828 633.4577 849.8298
2012.008 741.6437 666.0439 817.2435 626.0238 857.2637
2012.011 741.6437 661.4774 821.8101 619.0398 864.2476
2012.014 741.6437 657.1573 826.1302 612.4328 870.8547
2012.016 741.6437 653.0476 830.2399 606.1476 877.1399
2012.019 741.6437 649.1202 834.1672 600.1413 883.1462
2012.022 741.6437 645.3530 837.9345 594.3797 888.9078
2012.025 741.6437 641.7276 841.5599 588.8352 894.4523
Hopefully this helps you understand the problem with your original approach and improves your capacity with dealing with time series in R.
Final, and more accurate solution--because I'm avoiding other work that I should actually be doing right now...
Use the lubridate
package for better date handling:
require(lubridate)
y = ts(x$Used, start=c(2011, yday("2011-11-01")), frequency=365)
plot(forecast(ets(y), 10), xaxt="n")
a = seq(as.Date("2011-11-01"), by="weeks", length=11)
axis(1, at = decimal_date(a), labels = format(a, "%Y %b %d"), cex.axis=0.6)
abline(v = decimal_date(a), col='grey', lwd=0.5)
Resulting in:
Note the alternative method of identifying the start date for your ts
object.