Time Series Regression - arima¶

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)
ts <- ts_data(sin_data$y, 0)
ts_head(ts, 3)

A matrix: 3 × 1 of type dbl
t0
0.0000000
0.2474040
0.4794255

In [3]:

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

data sampling¶

In [4]:

samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

Model training¶

In [5]:

model <- ts_arima()
model <- fit(model, x=io_train$input, y=io_train$output)

Evaluation of adjustment¶

In [6]:

adjust <- predict(model, io_train$input)
adjust <- as.vector(adjust)
output <- as.vector(io_train$output)
ev_adjust <- evaluate(model, output, adjust)
ev_adjust$mse

0.0285768645342128

Prediction of test¶

In [7]:

prediction <- predict(model, x=io_test$input[1,], steps_ahead=5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)
ev_test <- evaluate(model, output, prediction)
ev_test

$values

0.412118485241757
0.173889485380434
-0.0751511204618093
-0.319519193622274
-0.54402111088937

$prediction

0.601137422519967
0.578441403730991
0.556602275978195
0.535587687233026
0.515366506921828

$smape

1.48971098704162

$mse

0.490402533932429

$R2

-3.23563201561947

$metrics

A data.frame: 1 × 3
mse	smape	R2
<dbl>	<dbl>	<dbl>
0.4904025	1.489711	-3.235632

Plot results¶

In [8]:

yvalues <- c(io_train$output, io_test$output)
plot_ts_pred(y=yvalues, yadj=adjust, ypre=prediction) + theme(text = element_text(size=16))

In [ ]: