Forecast time-series with two seasonal patterns

I am working on this problem for my research. The attached time-series represents the disk usage sampled every 5 mins. The series is a saw-tooth pattern since the disk usage keeps increasing and part of the disk gets backed up every 24 hrs. Thus there is linear trend as well as seasonal pattern. I see a hourly seasonal pattern as well, not sure about the reason. My goal is to use few days worth of disk usage and predict the mean disk usage with conf. interval for the next 24 hrs. I am currently analyzing 3 days worth of data (training set) to predict the next 24 hrs of data (test set).

The following R code sets up the data.

timestamp <- df_diskusage$time usage <- df_diskusage$usage

training_Set_size <- 0.75
training_set_index <- round(length(timestamp)*training_Set_size)
timestamp_train <- timestamp[1:training_set_index]
usage_train <- usage[1:training_set_index]
timestamp_test <- timestamp[-(1:training_set_index)]
usage_test <- usage[-(1:training_set_index)]

timestamp_posix <- as.POSIXct(timestamp, origin='1970-01-01', tz='EST')
timestamp_train_posix <- as.POSIXct(timestamp_train, origin='1970-01-01', tz='EST')
timestamp_test_posix <- as.POSIXct(timestamp_test, origin='1970-01-01', tz='EST')
#Since 12 sample points per hr, freq. = 12.
usage_train_ts <- ts(usage_train, frequency = 12)
usage_train_xts <- xts(usage_train, order.by = timestamp_train_posix)

usage_test_ts <- ts(usage_test, frequency = 12)
usage_test_xts <- xts(usage_test, order.by = timestamp_test_posix)
test_length <- length(usage_test)

tsdisplay(usage_train_xts) I have been trying various fitting techniques as listed in Dr. Hyndman's forecasting guide book (robjhyndman.com/uwafiles/fpp-notes.pdf), like SES, Holt's (and all variants) and ARIMA, but the forecasts were not good. Perhaps because of two seasonal patterns in the data. I thought of removing the hourly seasonal pattern, since I care only about predictions over next day. So I averaged data for each hour, and with 24 data points per day, I defined ts with frequency = 24.

getmeans  <- function(Df) {mean(Df$usage)} timestamp_posix_hours <- cut(timestamp_posix, breaks = "hours") temp_df <- data.frame(time=timestamp_posix_hours, usage=usage, hour=timestamp_posix_hours) temp_df_batch <- ddply(temp_df, .(hour), getmeans) temp_df_batch$hour <- as.POSIXct(temp_df_batch$hour, origin='1970-01-01', tz='EST') timestamp_hr_posix <- temp_df_batch$hour
usage_hr <- temp_df_batch$V1 #--------------------- training_hr_set_index <- round(length(timestamp_hr_posix)*training_Set_size) timestamp_hr_train_posix <- timestamp_hr_posix[1:training_hr_set_index] timestamp_hr_test_posix <- timestamp_hr_posix[-c(1:training_hr_set_index)] usage_hr_train <- usage_hr[1:training_hr_set_index] usage_hr_test <- usage_hr[-c(1:training_hr_set_index)] usage_hr_train_ts <- ts(usage_hr_train, frequency = 24) usage_hr_train_xts <- xts(usage_hr_train, order.by = timestamp_hr_train_posix) usage_hr_test_ts <- ts(usage_hr_test, frequency = 24) usage_hr_test_xts <- xts(usage_hr_test, order.by = timestamp_hr_test_posix) test_hr_length <- length(usage_hr_test) I evaluate the quality of fit on the prediction dataset (future 24 hrs) with various techniques, and "Holt-Winters technique with multiplicative seasonal, damped trend" and "STL + ETS(A,N,N)" provide the best fit. The results are consistent in cross-validation as well. I have a few questions at this point. 1. What is an elegant way of removing hourly seasonal pattern? I have averaged one sample per hour, which seems to leave a lot of artifacts. Any ideas? 2. For ARIMA, i use auto.arima(), and I am getting (0,1,1) as the best-fit. Why is it not picking any seasonal pattern? What settings I am going wrong with? fit_arima <- auto.arima(usage_hr_train_ts, max.P = 0, max.Q = 0, D = 0) fit_arima_forecast <- forecast(fit_arima, h = test_hr_length) plot(fit_arima_forecast) (Edited on 11/27/2016) On Dr. Hyndman's recommendation, I used TBATS technique. The predicted plot looks encouraging, but I am finding comparable Quality-of-fit with simpler models like Holt-winters. Plus TBATS takes a min or two to compute, which might be a bummer in my use case. I would prefer a much quicker model even with less accurate results. I would like to pursue techniques that remove the hourly seasonal pattern, so I can pursue "daily" seasonal pattern only. One way was to approximate data point every hour (as shown above). The prediction results are reasonably promising, but any other recommendations would be great. Another idea was smoothing data to remove hourly seasonal pattern, so I can the feed that data directly to Holt-winters (hw() in forecast) with frequency = 288, but such high frequencies are not supported. Any other ideas? 2 Answers You have a multiple seasonal time series with seasonalities of length$12$and$12\times 24=288\$. This is the sort of data for which the TBATS method was designed.

usage_train_ts <- msts(usage_train, seasonal.periods=c(12,288))
fit <- tbats(usage_train_ts)
fc <- forecast(fit)
plot(fc)

Details of the TBATS model are given here: http://robjhyndman.com/papers/complex-seasonality/

• Thanks Dr. Hyndman for your recommendation. With TBATS, I am seeing RMSE = 0.4036, MAE = 0.3211 for test data. However my best fit for test data is Holt-winters + multiplicative seasonal pattern, which provides RMSE = 0.3826, MAE = 0.2956. I have some more questions / comments in edited post. – tallharish Nov 27 '16 at 14:59

Please review Robust time-series regression for outlier detection as that problem/question is similar to yours in that there are two seasons in play. You have 12 readings per hour and 24 hours per day for three days or a total of 864 values.

What I might suggest is that you build a model for each of the 24 hours (step 1). With only three readings I would think that the simple average of three values) would be a good baseline forecast for the next day's hourly values. I would then develop a Transfer Function (ARMAX) model for each of the 12 readings per hour using the hourly total as the X variable and using the 24 predicted hourly values from step 1. As usual Outliers/Level Shifts/Time Trends should be identified along with the customized ARIMA structure for each of these 12 models (step2) using 72 historical values for Y and 3 distinct values for X . Forecasts can then computed for each of these 12 time slots using the hourly predictions from step 1.