Normalization Exponential Adaptive Normalization¶

In [1]:

# DAL ToolBox
# version 1.0.777

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform

Series for studying¶

In [2]:

data(sin_data)

In [3]:

library(ggplot2)
plot_ts(x=sin_data$x, y=sin_data$y) + theme(text = element_text(size=16))

sliding windows¶

In [4]:

sw_size <- 10
ts <- ts_data(sin_data$y, sw_size)
ts_head(ts, 3)
summary(ts[,10])

A matrix: 3 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.0000000	0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732
0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721
0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721	0.3816610

       t0          
 Min.   :-0.99929  
 1st Qu.:-0.55091  
 Median : 0.05397  
 Mean   : 0.02988  
 3rd Qu.: 0.63279  
 Max.   : 0.99460

In [5]:

library(ggplot2)
plot_ts(y=ts[,10]) + theme(text = element_text(size=16))

normalization¶

In [6]:

preproc <- ts_norm_an()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])
plot_ts(y=ts[1,]) + theme(text = element_text(size=16))

A matrix: 3 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.1770086	0.2931936	0.4021548	0.4971174	0.5721773	0.6226675	0.6454487	0.6391047	0.6040297	0.5424046
0.2650884	0.3740495	0.4690122	0.5440720	0.5945622	0.6173435	0.6109994	0.5759245	0.5142994	0.4299558
0.3677446	0.4627073	0.5377671	0.5882573	0.6110386	0.6046945	0.5696195	0.5079945	0.4236508	0.3218327

       t0         
 Min.   :0.04995  
 1st Qu.:0.15185  
 Median :0.41183  
 Mean   :0.44854  
 3rd Qu.:0.73197  
 Max.   :0.94995

In [ ]: