[R] Time Series forecasting models for irregular time series for zoo objects
Hi I am new with R.Currently I am using time series forecasting to do daily forecasting for predicting request per day.The dataset which i am using has unevenly spaced date a snapshot of dataset is given below I have to find the best model which can help me in predicting the future request which can make forecast for next 15 days.I have used zoo package for dealing with irregular time series data but now I am unable to implement "ets" and "ar" and "arima" model with this as it expects the series to be regular and evenly spaced and it gives me warning " Missing values encountered. Using longest contiguous portion of time series" but still I proceed for forecast then it is showing me date in the format like "1.711600e+04" and point of forecast is same for nearly all the dates. date req_per_day 13-07-2016 1 15-07-2016 1 11-08-2016 1 01-09-2016 1 13-09-2016 1 14-09-2016 1 22-09-2016 2 23-09-2016 1 26-09-2016 2 27-09-2016 4 29-09-2016 2 My question is how to implement ets and other model on zoo objects which show me correct date format.Based on other suggestions i have also tried converting zoo to ts but it is again showing "NA" for the dates which are not present in the dataset and give same result which I specified above. How to do time series forecasting with uneven or irregular dates using zoo objects in R. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Time series forecasting
Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 12 21 31 416 59 66 77 811 911 101 1112 1214 1313 142 154 165 1714 186 194 207 215 228 237 2415 2511 263 274 286 298 304 31?? (a known) For backtesting of forecasting accuracy, I can use either a sliding window ( 1 - 30 to solve for 31, 2 - 31 to solve for 32, 3 - 32 to solve for 33, etc.) OR a cumulative window (1 - 30 to solve for 31, 1 - 31 to solve for 32, 1 - 32 to solve for 33, etc.), whichever works better. I can also supply different windows if deemed appropriate, e.g., 50 or 75 or 100 obs, whatever, in either configuration. The 30 obs window is selected for this list query only so as not to take up too much message space. Query: How would you solve for ob 31 in the above series, with the constraints stated? (If you need a longer history, say, 50 obs or more, I can supply it off-list.) I've tried all the relevant Excel functions with no success, and suspect that the solution lies in some combination of them. Here I defer to the collective wisdom of you all. Once the correct concept is established, I can proceed to set it up in R for this and other similar series. Many TIA and regards, Perry E. Gary Tokyo [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Time series forecasting
Hi Perry, my impression after a very cursory glance: this looks like noise. Perhaps you should think a little more about your series - what kind of seasonality there could be (is this weekly data? or monthly?), whether the peaks and troughs could be due to some kind of external driver, whether you really have count data, that kind of thing. Until then, there is little else to do than to use a very simple method, e.g. forecast the last observation (random walk) or the mean of the observations (white noise), or the median. All of these benchmarks can be surprisingly hard to beat. If you have seasonality but no external influence, you could look at smoothing methods, they are nice to interpret and usually perform very well. I'd recommend Hyndman et al., Forecasting with Exponential Smoothing: The State Space Approach and the accompanying forecast R package, mainly with the ets() function. You could also look at arima(). I fitted an ARIMA model to your data, and as expected, it returned a simple mean (not that I would recommend blindly fitting ARIMA to just any data): Call: arima(x = foo[, 2]) Coefficients: intercept 7.2333 s.e. 0.8009 sigma^2 estimated as 19.25: log likelihood = -86.93, aic = 177.85 And for count data, you could use some variants of ARIMA, e.g., INAR. HTH, Stephan pg...@gol.com schrieb: Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 12 21 31 416 59 66 77 811 911 101 1112 1214 1313 142 154 165 1714 186 194 207 215 228 237 2415 2511 263 274 286 298 304 31?? (a known) For backtesting of forecasting accuracy, I can use either a sliding window ( 1 - 30 to solve for 31, 2 - 31 to solve for 32, 3 - 32 to solve for 33, etc.) OR a cumulative window (1 - 30 to solve for 31, 1 - 31 to solve for 32, 1 - 32 to solve for 33, etc.), whichever works better. I can also supply different windows if deemed appropriate, e.g., 50 or 75 or 100 obs, whatever, in either configuration. The 30 obs window is selected for this list query only so as not to take up too much message space. Query: How would you solve for ob 31 in the above series, with the constraints stated? (If you need a longer history, say, 50 obs or more, I can supply it off-list.) I've tried all the relevant Excel functions with no success, and suspect that the solution lies in some combination of them. Here I defer to the collective wisdom of you all. Once the correct concept is established, I can proceed to set it up in R for this and other similar series. Many TIA and regards, Perry E. Gary Tokyo [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
