I am trying to make a prediction on the number of visitors of a website and am wondering if it can be done based only on categorical time variables. I received some guidance on how to structure my input after a question on Stack Overflow, but I still can't make predictions close to the real values, no matter what I've tried and how I've combined the data.

Here are the variables in my dataset:

website_id

date_int – would act as a time index

month – would be used for seasonal effect

type – variable derived from the response variable (number of visits), representing the size of the website by calculating an average of number of visits (ranges from 1 to 5).

D1, D2, D3, D4, D5, D6 – variables used to capture the "seasonal effect" for the day of week.

visits

I'm leaving also a link to a sample data in case it might be relevant: train.csv.

This is what the code I've tried in R looks like.

`train = read.csv("trainData.csv", header = TRUE) head(train) dates <- as.factor(train$date_int) dates months <- as.factor(train$month) months model<-glm(visits ~ dates + type , train, family=poisson) summary(model) P = predict(model, newdata = train, type = "response") imp = round(P) imp `

I'm new to R, but from what I've seen, all the examples, even those that should be similar (like estimating sales), use other variables beside those categorical time variables. I don't have other features to base my prediction on, so I feel the need to ask if a prediction can even be made using the input given in this situation?

**Contents**hide

#### Best Answer

I recommend reading "Forecasting: principles and practice" https://www.otexts.org/fpp/8/2 and there are good R libs for handling your data. Using those tools, I would generate a unique forecast for each website.

`require(forecast) w=99 WWWusage=ts(train$visits[train$website_id==w],frequency = 7) fit <- auto.arima(WWWusage) plot(forecast(fit,h=32)) `

So to build all of them:

`for(w in sort(unique(train$website_id))){ WWWusage=ts(train$visits[train$website_id==w],frequency=7) fit <- auto.arima(WWWusage) plot(forecast(fit,h=32)) title(paste("nnwebsite",w)) } `

If you want to get some insight on how autoregression works, I wrote this code:

`w=99 train = read.csv("/tmp/trainData.csv", header = TRUE) train=train[train$website_id==w,] repair=function(x) { if(length(x)==0) return(NA); return(x);} for(b in seq(7*5,7*9,7)){ train[[paste0('backshift',b)]]=rep(NA,nrow(train)) for(r in 1:nrow(train)){ #slow way to do this train[r,paste0('backshift',b)]=repair(train$visits[train$website_id==train[r,'website_id'] & train$date_int==(train[r,'date_int']-b)]) } } train=train[complete.cases(train),] rmse=function(x,y,k=0){ return( sqrt(sum((x-y)^2)/(length(x)-k))) } require(MASS) train$months <- as.factor(train$month) train$date=NULL model<-glm(visits ~. , train, family=poisson) model=stepAIC(model,trace=F) summary(model) P = predict(model, newdata = train, type = "response") imp = round(P) rmse(imp,train$visits) train$fit=imp with(train[train$website_id==w,],{ plot(date_int,visits,type='l') points(date_int,fit,col='red',type='l') title(w) }) `

Here is the poisson model:

`Call: glm(formula = visits ~ date_int + D1 + D2 + D3 + D4 + D5 + D6 + backshift35 + backshift42 + backshift49 + backshift56 + backshift63 + months, family = poisson, data = train) Deviance Residuals: Min 1Q Median 3Q Max -72.386 -4.389 1.126 6.912 54.939 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.003e+01 2.405e-02 416.935 < 2e-16 *** date_int -3.380e-03 1.125e-04 -30.044 < 2e-16 *** D1 -4.000e-02 3.425e-03 -11.680 < 2e-16 *** D2 7.957e-01 9.623e-03 82.686 < 2e-16 *** D3 6.755e-01 8.258e-03 81.800 < 2e-16 *** D4 6.502e-01 7.997e-03 81.299 < 2e-16 *** D5 5.791e-01 7.470e-03 77.530 < 2e-16 *** D6 4.544e-01 6.173e-03 73.602 < 2e-16 *** backshift35 9.173e-06 5.502e-07 16.671 < 2e-16 *** backshift42 -1.368e-05 5.191e-07 -26.353 < 2e-16 *** backshift49 1.408e-06 5.656e-07 2.489 0.01280 * backshift56 -2.305e-05 6.010e-07 -38.358 < 2e-16 *** backshift63 1.957e-06 6.035e-07 3.242 0.00119 ** months9 -3.231e-01 1.332e-02 -24.258 < 2e-16 *** months10 -2.589e-01 1.070e-02 -24.192 < 2e-16 *** months11 -2.907e-01 7.710e-03 -37.706 < 2e-16 *** months12 -3.915e-01 4.577e-03 -85.529 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 157970 on 134 degrees of freedom Residual deviance: 39894 on 118 degrees of freedom AIC: 41448 Number of Fisher Scoring iterations: 4 `

You can try different values of website_id (w). I had backshift start at 5 weeks ago which allows you to easily forecast 5 weeks, but you can forecast farther than that by making predictions based on predictions.