A set of tables containing the stock price and the residuals of the Prophet model over a daily or monthly period. This data was used to train the LightGBM model.
Format
A tibble with 63,617 / 57,793 rows and 4 variables.
- ticker
The ticker/symbol of the stock
- ref_date
The date on which the price was recorded
- price
The price of the stock (USD)
- residuals
The residuals (difference between predictions and actual price) of the Prophet model
An object of class tbl_df (inherits from tbl, data.frame) with 58056 rows and 4 columns.
Examples
daily_training_data
#> # A tibble: 63,252 × 4
#> ticker ref_date price_adjusted residuals
#> <fct> <date> <dbl> <dbl>
#> 1 A 2022-06-13 116. 1.62
#> 2 A 2022-06-14 116. 1.33
#> 3 A 2022-06-15 116. 1.03
#> 4 A 2022-06-16 115. -0.978
#> 5 A 2022-06-17 112. -3.26
#> 6 A 2022-06-21 113. -2.26
#> 7 A 2022-06-22 113. -3.09
#> 8 A 2022-06-23 118. 1.18
#> 9 A 2022-06-24 121. 3.82
#> 10 A 2022-06-27 120. 2.98
#> # … with 63,242 more rows
monthly_training_data
#> # A tibble: 58,056 × 4
#> ticker ref_date price_adjusted residuals
#> <fct> <date> <dbl> <dbl>
#> 1 A 2012-12-11 26.9 2.26
#> 2 A 2013-01-02 29.4 3.33
#> 3 A 2013-02-01 27.2 1.18
#> 4 A 2013-03-01 27.6 1.14
#> 5 A 2013-04-01 27.3 1.07
#> 6 A 2013-05-01 29.9 3.84
#> 7 A 2013-06-03 28.2 2.21
#> 8 A 2013-07-01 29.5 0.228
#> 9 A 2013-08-01 30.8 1.05
#> 10 A 2013-09-03 33.9 4.82
#> # … with 58,046 more rows