Dhivya Narayanasamy
2017-Apr-27 05:40 UTC
[R] Problem in conversion of regulate time series and forecasting using Date Time [Timestamp values]:R
Hi, 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[[alternative HTML version deleted]]