[R] Problem in conversion of regulate time series and forecasting using Date Time [Timestamp values]:R

[Dhivya Narayanasamy]

Hi,
I am new to R. Kindly help me with the plot that gives wrong x-axis
values.  I have a data frame "gg", that looks like this:

> head(gg)

           timestamps      value
1 2017-04-25 16:52:00 -0.4120000
2 2017-04-25 16:53:00 -0.4526667
3 2017-04-25 16:54:00 -0.4586667
4 2017-04-25 16:55:00 -0.4606667
5 2017-04-25 16:56:00 -0.5053333
6 2017-04-25 16:57:00 -0.5066667

I need to plot this as a Time series data to do forecasting. The steps are
as follows:

1) gg$timestamps <- as.POSIXct(gg$timestamps, format = "%Y-%m-%d %H-%M-%S")
 #changing "Timestamps" column 'factor' to 'as.POSIXct'.

2) gg.ts <- xts(x=gg$value, order.by = gg$timestamps) #converting the
dataframe to time series (Non Regular Time series)

3) fitting <- auto.arima(gg.ts) #fitting the time series model using
auto.arima

4) fore <- forecast(fitting, h=30, level = c(80,95))  #Forecasting

5) I am using plotly to this forecast model (Inspired from here :
https://plot.ly/r/graphing-multiple-chart-types/#plotting-forecast-objects)

plot_ly() %>%
  add_lines(x = time(gg.ts), y = gg.ts,
            color = I("black"), name = "observed") %>%
  add_ribbons(x = time(fore$mean), ymin = fore$lower[, 2], ymax =
fore$upper[, 2],
              color = I("gray95"), name = "95% confidence") %>%
  add_ribbons(x = time(fore$mean), ymin = fore$lower[, 1], ymax =
fore$upper[, 1],
              color = I("gray80"), name = "80% confidence") %>%
  add_lines(x = time(fore$mean), y = fore$mean, color = I("blue"), name =
"prediction")


The plot comes out wrong: 1) x axis labels are wrong. It shows some
irrelevant values on axis. 2) the plot is not coming out.
Also I tried to convert "gg.ts" to a regulate time series which throws
error :

> gg.xts <- ts(gg.ts, frequency = '1', start = ('2017-04-25 16:52:00'))
Error in 1/frequency : non-numeric argument to binary operator

Please help me how to use Date Time values in converting to regulate time
series for forecasting.


Regards
> Dhivya

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