https://github.com/krzjoa/torchts

Time series with torch
https://github.com/krzjoa/torchts

deep-learning deep-neural-networks forecasting neural-networks r time-series time-series-forecasting torch

Last synced: about 4 hours ago
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Time series with torch

• Host: GitHub
• URL: https://github.com/krzjoa/torchts
• Owner: krzjoa
• License: other
• Created: 2020-12-15T23:12:56.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-06-18T23:41:27.000Z (over 2 years ago)
• Last Synced: 2024-01-30T15:44:21.432Z (9 months ago)
• Topics: deep-learning, deep-neural-networks, forecasting, neural-networks, r, time-series, time-series-forecasting, torch
• Language: R
• Homepage: https://krzjoa.github.io/torchts/
• Size: 2.37 MB
• Stars: 13
• Watchers: 7
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

always_allow_html: true

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# torchts 

[![CRAN status](https://www.r-pkg.org/badges/version/torchts)](https://CRAN.R-project.org/package=torchts)

[![R build status](https://github.com/krzjoa/torchts/workflows/R-CMD-check/badge.svg)](https://github.com/krzjoa/torchts/actions)

[![Codecov test coverage](https://codecov.io/gh/krzjoa/torchts/branch/master/graph/badge.svg)](https://codecov.io/gh/krzjoa/torchts?branch=master)

[![Buy hex

stciker](https://img.shields.io/badge/buy%20hex-torchts-green)](https://www.redbubble.com/i/sticker/torchts-R-package-hex-sticker-by-krzjoa/93537989.EJUG5)

> Time series models with torch

[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/kjoachimiak)

## Installation

You can install the released version of torchts from [CRAN](https://CRAN.R-project.org) with:

The development version from [GitHub](https://github.com/) with:

``` r

# install.packages("devtools")

devtools::install_github("krzjoa/torchts")

```

## parsnip models

```{r parsnip.api}

library(torchts)

library(torch)

library(rsample)

library(dplyr, warn.conflicts = FALSE)

library(parsnip)

library(timetk)

library(ggplot2)

tarnow_temp <- 

  weather_pl %>% 

  filter(station == "TRN") %>% 

  select(date, tmax_daily)

# Params

EPOCHS    <- 3

HORIZON   <- 1

TIMESTEPS <- 28

# Splitting on training and test

data_split <- 

  time_series_split(

    tarnow_temp, date, 

    initial = "18 years",

    assess  = "2 years", 

    lag     = TIMESTEPS

  )

# Training 

rnn_model <- 

  rnn(

    timesteps    = TIMESTEPS,

    horizon      = HORIZON,

    epochs       = EPOCHS,

    learn_rate   = 0.01,

    hidden_units = 20,

    batch_size   = 32,

    scale        = TRUE 

  ) %>% 

  set_device('cpu')  %>% 

  fit(tmax_daily ~ date, 

      data = training(data_split))

prediction <-

  rnn_model %>%

  predict(new_data = testing(data_split))

plot_forecast(

  data     = testing(data_split),

  forecast = prediction,

  outcome  = tmax_daily

)

```

## Transforming data.frames to tensors

In `as_tensor` function we can specify columns, that are used to 

create a tensor out of the input `data.frame`. Listed column names 

are only used to determine dimension sizes - they are removed after that

and are not present in the final tensor.

```{r example}

temperature_pl <- 

  weather_pl %>% 

  select(station, date, tmax_daily)

# Expected shape

c(

  n_distinct(temperature_pl$station),

  n_distinct(temperature_pl$date),

  1

)

temperature_tensor <- 

  temperature_pl %>% 

  as_tensor(station, date)

dim(temperature_tensor)

temperature_tensor[1, 1:10]

temperature_pl %>% 

  filter(station == "SWK") %>% 

  arrange(date) %>% 

  head(10)

```

## Similar projects in Python 

* [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/stable/)

* [PyTorchTS](https://github.com/zalandoresearch/pytorch-ts)

* [TorchTS](https://rose-stl-lab.github.io/torchTS/)

* [GluonTS ](https://ts.gluon.ai/)

* [sktime-dl](https://github.com/sktime/sktime-dl)