inst/doc/textforecast.R

In TextForecast: Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>" , warning=FALSE, eval = FALSE
)

## ----get words example--------------------------------------------------------
#  ## Example from function get_words.
#  library(TextForecast)
#  st_year=2017
#  end_year=2018
#  path_name=system.file("news",package="TextForecast")
#  qt=paste0(sort(rep(seq(from=st_year,to=end_year,by=1),12)),
#  c("m1","m2","m3","m4","m5","m6","m7","m8","m9","m10","m11","m12"))
#  z_wrd=get_words(corpus_dates=qt[1:6],path_name=path_name,ntrms=10,st=0)
#  zz=z_wrd[[2]]
#  head(zz)

## ----get collocations example-------------------------------------------------
#  library(TextForecast)
#  st_year=2017
#  end_year=2018
#  path_name=system.file("news",package="TextForecast")
#  qt=paste0(sort(rep(seq(from=st_year,to=end_year,by=1),12)),
#  c("m1","m2","m3","m4","m5","m6","m7","m8","m9","m10","m11","m12"))
#  z_coll=get_collocations(corpus_dates=qt[1:23],path_name=path_name,
#  ntrms=20,ngrams_number=3,min_freq=10)
#  zz=z_coll[[2]]
#  #head(zz)
#  knitr::kable(head(zz, 23))
#  

## ----get terms example--------------------------------------------------------
#  library(TextForecast)
#  st_year=2017
#  end_year=2018
#  path_name=system.file("news",package="TextForecast")
#  qt=paste0(sort(rep(seq(from=st_year,to=end_year,by=1),12)),
#  c("m1","m2","m3","m4","m5","m6","m7","m8","m9","m10","m11","m12"))
#  z_terms=get_terms(corpus_dates=qt[1:23],path.name=path_name,ntrms_words=10,
#  ngrams_number=3,st=0,ntrms_collocation=10,min_freq=10)
#  zz=z_terms[[2]]
#  #head(zz,23)
#  knitr::kable(head(zz, 23))

## ----tf-idf example-----------------------------------------------------------
#  library(TextForecast)
#  data("news_data")
#   X=as.matrix(news_data[,2:ncol(news_data)])
#    tf_idf=tf_idf(X)
#    head(tf_idf[[1]])

## ----optimal alphas example---------------------------------------------------
#  library(TextForecast)
#   set.seed(1)
#   data("stock_data")
#   data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   X=news_data[,2:ncol(news_data)]
#   x=as.matrix(X)
#   grid_alphas=seq(by=0.05,to=0.95,from=0.05)
#   cont_folds=TRUE
#   t=length(y)
#   optimal_alphas=optimal_alphas(x[1:(t-1),],w[1:(t-1),],y[2:t],grid_alphas,TRUE,"gaussian")
#   print(optimal_alphas)

## ----tv dictionary example----------------------------------------------------
#   library(TextForecast)
#   set.seed(1)
#   data("stock_data")
#   data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   X=news_data[,2:ncol(news_data)]
#   x=as.matrix(X)
#   grid_alphas=seq(by=0.05,to=0.95,from=0.05)
#   cont_folds=TRUE
#   t=length(y)
#   optimal_alphas=optimal_alphas(x=x[1:(t-1),],w=w[1:(t-1),],y=y[2:t],grid_alphas=grid_alphas,cont_folds=TRUE,family="gaussian")
#   x_star=tv_dictionary(x=x[1:(t-1),],w=w[1:(t-1),],y=y[2:t],alpha=optimal_alphas[1],lambda=optimal_alphas[2],newx=x,family="gaussian")
#   optimal_alphas1=optimal_alphas(x=x[1:(t-1),],y=y[2:t],grid_alphas=grid_alphas,cont_folds=TRUE,family="gaussian")
#   x_star1=tv_dictionary(x=x[1:(t-1),],y=y[2:t],alpha=optimal_alphas1[1],lambda=optimal_alphas1[2],newx=x,family="gaussian")

## ----optimal factor example---------------------------------------------------
#  library(TextForecast)
#  data("optimal_x")
#  optimal_factor <- TextForecast::optimal_factors(optimal_x,8)
#  head(optimal_factor[[1]])

## ----hard thresholding example------------------------------------------------
#  library(TextForecast)
#  data("stock_data")
#  data("optimal_factors")
#  y=as.matrix(stock_data[,2])
#  y=as.vector(y)
#  w=as.matrix(stock_data[,3])
#  pc=as.matrix(optimal_factors)
#  t=length(y)
#  news_factor <- hard_thresholding(w=w[1:(t-1),],
#  x=pc[1:(t-1),],y=y[2:t],p_value = 0.01,newx = pc)

## ----Text Forecast Example----------------------------------------------------
#  library(TextForecast)
#  set.seed(1)
#  data("stock_data")
#  y=as.matrix(stock_data[,2])
#  w=as.matrix(stock_data[,3])
#  data("optimal_factors_data")
#  pc=as.matrix(optimal_factors)
#  z=cbind(w,pc)
#  fcsts=text_forecast(z,y,1,TRUE)
#  print(fcsts)

## ----Text Nowcast Example-----------------------------------------------------
#   library(TextForecast)
#   set.seed(1)
#   data("stock_data")
#    data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   data("optimal_factors_data")
#   pc=as.matrix(optimal_factors)
#   z=cbind(w,pc)
#   t=length(y)
#   ncsts=text_nowcast(z,y[1:(t-1)],TRUE)
#   print(ncsts)

## ----Top Terms Example--------------------------------------------------------
#  library(TextForecast)
#   set.seed(1)
#   data("stock_data")
#   data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   X=news_data[,2:ncol(news_data)]
#   x=as.matrix(X)
#   grid_alphas=seq(by=0.05,to=0.95,from=0.05)
#   cont_folds=TRUE
#   t=length(y)
#   optimal_alphas=optimal_alphas(x[1:(t-1),],w[1:(t-1),],
#   y[2:t],grid_alphas,TRUE,"gaussian")
#   top_trms<- top_terms(x[1:(t-1),],w[1:(t-1),],y[2:t],optimal_alphas[[1]],
#  optimal_alphas[[2]],10,TRUE,10,c(2,0.3),.15,"gaussian")

## ----TV sentiment index example-----------------------------------------------
#  library(TextForecast)
#   set.seed(1)
#   data("stock_data")
#   data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   X=news_data[,2:ncol(news_data)]
#   x=as.matrix(X)
#   grid_alphas=seq(by=0.05,to=0.95,from=0.05)
#   cont_folds=TRUE
#   t=length(y)
#   optimal_alphas=optimal_alphas(x[1:(t-1),],w[1:(t-1),],
#   y[2:t],grid_alphas,TRUE,"gaussian")
#    tv_index <- tv_sentiment_index(x[1:(t-1),],w[1:(t-1),],
#   y[2:t],optimal_alphas[[1]],optimal_alphas[[2]],x,"gaussian",2)
#   head(tv_index)

## ----TV sentiment index all coefs example-------------------------------------
#   set.seed(1)
#   data("stock_data")
#   data("news_data")
#   y=as.matrix(stock_data[,2])
#   w=as.matrix(stock_data[,3])
#   data("news_data")
#   X=news_data[,2:ncol(news_data)]
#   x=as.matrix(X)
#   grid_alphas=0.15
#   cont_folds=TRUE
#   t=length(y)
#   optimal_alphas=optimal_alphas(x=x[1:(t-1),],
#                                 y=y[2:t],grid_alphas=grid_alphas,cont_folds=TRUE,family="gaussian")
#   tv_idx=tv_sentiment_index_all_coefs(x=x[1:(t-1),],y=y[2:t],alpha = optimal_alphas[1],lambda = optimal_alphas[2],newx=x,
#                                    scaled = TRUE,k_mov_avg = 4,type_mov_avg = "s")