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## ----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")
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