R anomaly detection

R anomaly detection

#Install packages
install_github("petermeissner/wikipediatrend")
install_github("twitter/AnomalyDetection")

library(devtools)
library(Rcpp)
library(wikipediatrend)
library(AnomalyDetection)

#load data
fifa_data_wikipedia = wikipediatrend::wp_trend("fifa", from = "2013-03-18", lang = "en")
head(fifa_data_wikipedia)

#Plotting data
library(ggplot2)
ggplot(fifa_data_wikipedia, aes(x = date, y = views,
                                color = views)) + geom_line()

# Keep only date & page views and discard all other variables
columns_to_keep=c("date","views")
fifa_data_wikipedia=fifa_data_wikipedia[,columns_to_keep]

#Anomaly detection
anomalies = AnomalyDetectionTs(fifa_data_wikipedia, direction = "pos",#positive
                               plot = TRUE)
anomalies$plot



install_github("business-science/anomalize")
library(anomalize)
library(tidyverse)
library(coindeskr)

bitcoin_data <- get_historic_price(start = "2017-01-01")
bitcoin_data_ts = bitcoin_data %>% rownames_to_column() %>% 
  as.tibble() %>% mutate(date = as.Date(rowname)) %>% select(-one_of('rowname'))

#decompose
bitcoin_data_ts %>% time_decompose(Price, method = "stl", frequency = "auto", trend = "auto") %>%
anomalize(remainder, method = "gesd", alpha = 0.05, max_anoms = 0.1) %>% plot_anomaly_decomposition()

#removed trend and sensonality
bitcoin_data_ts %>% time_decompose(Price) %>% anomalize(remainder) %>% 
  time_recompose() %>%  plot_anomalies(time_recomposed = TRUE, ncol = 3, alpha_dots = 0.5)

#getting the annomalies
bitcoin_data_ts %>% time_decompose(Price) %>%
  anomalize(remainder) %>%  time_recompose() %>%  filter(anomaly == 'Yes')