Time Series Forecast - Evaluating accuracy I am very new to R and the forecast package authored by Rob Hyndman.
I am working on a time series with 24 samples per hour. I trained a random forest regressor to forecast 6 hour ahead values and am using MAPE(Mean Absolute Percentage Error) on a held out duration as the accuracy metric. 
I want to compare its accuracy with standard time series methods like ARMA and ARIMA models.
Time Series Sample

Here is what I have currently. data.csv has 576*16(16 days worth) samples and I wish to measure forecast accuracy on last 3 days.
library(forecast)
pv_data = read.csv("data.csv", header=FALSE)
pv_ts <- ts(pv_data$V2)
train_ts <- window(pv_ts, end=576*13)
test_ts <- window(pv_ts, start=576*13+1)
fit <- auto.arima(train_ts)
accuracy(forecast(fit, h=576*3), test_ts)

This gives me MAPE  which is average of h = 1 to 576*3 samples ahead point forecast absolute errors. 
Question: How to find the average of h=144 ahead forecast absolute percent error of the estimates of samples in test_ts? Specifically, how to calculate $ \frac {\sum\limits_{T=576*13-h}^{576*16-h} \left\lvert \tfrac{\hat{e}_{T+h}}{y_{T+h}}\right\rvert }{576*3}$ with h=144 where  $\hat{e}_{T+h}=\hat{y}_{T+h | T}-y_{T+h}$?
 A: I'm not really sure what you're trying to achieve here. The forecast at h=144 is just a point estimate whereas the MAPE is a measure of average accuracy over a selection of point forecasts. 
As in your case, the sequence of point forcasts from h=1 to h=576*3is used to calculate the MAPE in your current example. If you want to evaluate the accuracy of your 6-hour ahead forecast, why not just look at the forecast error? I.e. $\hat{e}_{T+h}=\hat{y}_{T+h | T}-y_{T+h}$
EDIT: Added a rough outline to a solution of what I think is your problem. You mention calculating the "average" for h=144 but the average is equal to the point estimate because you only have one forecast at this particular horizon. 
library(forecast)
pv_data = read.csv("data.csv", header=FALSE)
pv_ts <- ts(pv_data$V2)
train_ts <- window(pv_ts, end=576*13)
test_ts <- window(pv_ts, start=576*13+1)
fit <- auto.arima(train_ts)

#Store point forecasts
fc <- forecast(fit, h=576*3)$mean

# Store actual outcome
y <- window(pv_ts, start=576*13+144, end=576*13+144)

# Store point forecast for h=144
y_hat <- fc[144]

# Calculate ape at h=144
ape <- abs((y_hat-y)/y)

