** Predicting the Trend of Amazon Fire TV for 2017**

Method

I will use wikipedia trend and facebook prophet for this prediction:

library(wikipediatrend)
tail(data)
##                                                                          
## 160         if (!found)                                                  
## 161             warning(gettextf("data set %s not found", sQuote(name)), 
## 162                 domain = NA)                                         
## 163     }                                                                
## 164     invisible(names)                                                 
## 165 }
library(ggplot2)
summary(data)
##       date                count            lang          
##  Min.   :2014-01-01   Min.   :   0.0   Length:749        
##  1st Qu.:2014-07-07   1st Qu.: 317.0   Class :character  
##  Median :2015-01-10   Median : 395.0   Mode  :character  
##  Mean   :2015-01-10   Mean   : 420.6                     
##  3rd Qu.:2015-07-16   3rd Qu.: 509.0                     
##  Max.   :2016-01-20   Max.   :9454.0                     
##      page                rank       month              title          
##  Length:749         Min.   :-1   Length:749         Length:749        
##  Class :character   1st Qu.:-1   Class :character   Class :character  
##  Mode  :character   Median :-1   Mode  :character   Mode  :character  
##                     Mean   :-1                                        
##                     3rd Qu.:-1                                        
##                     Max.   :-1

The 0.00 value for the minimum means that data might not have been available. I will go ahead and remove it, replacing it with NA

data$count[data$count==0]<-NA

Also, I am going to assign “ds” to the date variable, “y” to the count variable and create a data frame that contains two columns with the above information.

ds<-data$date
y<-data$count
df<-data.frame(ds,y)
qplot(ds,y, data = data)
## Warning: Removed 93 rows containing missing values (geom_point).

Now, I am going to specify the data frame I want to use, create a future data frame and use that to make the prediction of the trend of Amazon Fire TV for 2017.

library(prophet)
## Loading required package: Rcpp
m<-prophet(df)
## Warning in set_auto_seasonalities(m): Disabling yearly seasonality. Run
## prophet with `yearly.seasonality=TRUE` to override this.
## Initial log joint probability = -2.75475
## Optimization terminated normally: 
##   Convergence detected: relative gradient magnitude is below tolerance
future<-make_future_dataframe(m, periods = 365)
tail(future)
##              ds
## 1109 2017-01-14
## 1110 2017-01-15
## 1111 2017-01-16
## 1112 2017-01-17
## 1113 2017-01-18
## 1114 2017-01-19

As, you can see from the datestamp above, now we have date for 2017.Let’s make the prediction for the count trend for Amazon Fire TV for 2017.

forecast<-predict(m,future)
plot(m, forecast)

Conclusion The popularity of Amazon Fire TV for 2017 will increase particularly on Fridays.

Let’s me show you in clearity of what I mean by the above using the graphs below:

prophet_plot_components(m, forecast)