Pytorch implementation of the ESRNN

## Project description

# Pytorch Implementation of the ES-RNN

Pytorch implementation of the ES-RNN algorithm proposed by Smyl, winning submission of the M4 Forecasting Competition. The class wraps fit and predict methods to facilitate interaction with Machine Learning pipelines along with evaluation and data wrangling utility. Developed by Autonlab’s members at Carnegie Mellon University.

## Installation Prerequisites

- numpy>=1.16.1
- pandas>=0.25.2
- pytorch>=1.3.1

## Installation

This code is a work in progress, any contributions or issues are welcome on GitHub at: https://github.com/kdgutier/esrnn_torch

You can install the *released version* of `ESRNN`

from the Python package index with:

```
pip install ESRNN
```

## Usage

### Input data

The fit method receives `X_df`

, `y_df`

training pandas dataframes in long format. Optionally `X_test_df`

and `y_test_df`

to compute out of sample performance.

`X_df`

must contain the columns`['unique_id', 'ds', 'x']`

`y_df`

must contain the columns`['unique_id', 'ds', 'y']`

`X_test_df`

must contain the columns`['unique_id', 'ds', 'x']`

`y_test_df`

must contain the columns`['unique_id', 'ds', 'y']`

and a benchmark model to compare against (default`'y_hat_naive2'`

).

For all the above:

- The column
`'unique_id'`

is a time series identifier, the column`'ds'`

stands for the datetime. - Column
`'x'`

is an exogenous categorical feature. - Column
`'y'`

is the target variable. - Column
`'y'`

**does not allow negative values**and the first entry for all series must be**grater than 0**.

The `X`

and `y`

dataframes must contain the same values for `'unique_id'`

, `'ds'`

columns and be **balanced**, ie.no *gaps* between dates for the frequency.

`X_df` |
`y_df` |
`X_test_df` |
`y_test_df` |
---|---|---|---|

### M4 example

```
from ESRNN.m4_data import prepare_m4_data
from ESRNN.utils_evaluation import evaluate_prediction_owa
from ESRNN import ESRNN
X_train_df, y_train_df, X_test_df, y_test_df = prepare_m4_data(dataset_name='Yearly',
directory = './data',
num_obs=1000)
# Instantiate model
model = ESRNN(max_epochs=25, freq_of_test=5, batch_size=4, learning_rate=1e-4,
per_series_lr_multip=0.8, lr_scheduler_step_size=10,
lr_decay=0.1, gradient_clipping_threshold=50,
rnn_weight_decay=0.0, level_variability_penalty=100,
testing_percentile=50, training_percentile=50,
ensemble=False, max_periods=25, seasonality=[],
input_size=4, output_size=6,
cell_type='LSTM', state_hsize=40,
dilations=[[1], [6]], add_nl_layer=False,
random_seed=1, device='cpu')
# Fit model
# If y_test_df is provided the model
# will evaluate predictions on
# this set every freq_test epochs
model.fit(X_train_df, y_train_df, X_test_df, y_test_df)
# Predict on test set
y_hat_df = model.predict(X_test_df)
# Evaluate predictions
final_owa, final_mase, final_smape = evaluate_prediction_owa(y_hat_df, y_train_df,
X_test_df, y_test_df,
naive2_seasonality=1)
```

## Overall Weighted Average

A metric that is useful for quantifying the aggregate error of a specific model for various time series is the Overall Weighted Average (OWA) proposed for the M4 competition. This metric is calculated by obtaining the average of the symmetric mean absolute percentage error (sMAPE) and the mean absolute scaled error (MASE) for all the time series of the model and also calculating it for the Naive2 predictions. Both sMAPE and MASE are scale independent. These measurements are calculated as follows:

## Current Results

Here we used the model directly to compare to the original implementation. It is worth noticing that these results do not include the ensemble methods mentioned in the ESRNN paper.

Results of the M4 competition.

DATASET | OUR OWA | M4 OWA (Smyl) |
---|---|---|

Yearly | 0.785 | 0.778 |

Quarterly | 0.879 | 0.847 |

Monthly | 0.872 | 0.836 |

Hourly | 0.615 | 0.920 |

Weekly | 0.952 | 0.920 |

Daily | 0.968 | 0.920 |

## Replicating M4 results

Replicating the M4 results is as easy as running the following line of code (for each frequency) after installing the package via pip:

```
python -m ESRNN.m4_run --dataset 'Yearly' --results_directory '/some/path' \
--gpu_id 0 --use_cpu 0
```

Use `--help`

to get the description of each argument:

```
python -m ESRNN.m4_run --help
```

## Authors

This repository was developed with joint efforts from AutonLab researchers at Carnegie Mellon University and Orax data scientists.

**Kin Gutierrez**- kdgutier**Cristian Challu**- cristianchallu**Federico Garza**- FedericoGarza - mail**Max Mergenthaler**- mergenthaler

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## REFERENCES

- A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
- The M4 Competition: Results, findings, conclusion and way forward
- M4 Competition Data
- Dilated Recurrent Neural Networks
- Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
- A Dual-Stage Attention-Based recurrent neural network for time series prediction

## Project details

## Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.