I'm running the code nearly unchanged on a Google Cloud Compute instance with 2x Nvidia V100, 60GB RAM, 16CPUs. config.py is unchanged.

With 15 epochs on the Quarterly data, total training time is 16.01 minutes, almost double the 8.94minutes shown in the paper. However, the validation results at the end of epoch 15 are nearly identical to the paper's reported results:

{'Demographic': 10.814908027648926, 'Finance': 10.71678638458252, 'Industry': 7.436440944671631, 'Macro': 9.547700881958008, 'Micro': 11.63847827911377, 'Other': 7.911505699157715, 'Overall': 10.091866493225098, 'loss': 7.8162946701049805}

When I remove the model saving step, training time decreased to 15.76 minutes.

I downloaded the dataset from the provided link, and made no changes.

I'm using updated package versions, although I wouldn't expect this to halve performance:

• pytorch 1.2
• tensorflow 1.14.0

What hardware configuration was the authors' testing done on? I'm using dual V100s, the highest-end GPUs available on GCP. I'd expect to match or outperform the reported benchmarks. Do you have any thoughts on why my performance is considerably worse in my situation?

Great work on this project! Having used a version of the original ES-RNN code, I have a few questions about the differences between the implementations and the results presented in the paper.

1. The paper mentions "Note that for monthly data, Smyl et al. (2018) were running the algorithm of 6 pairs of 2 workers and for quarterly data, 4 pairs of 2 workers were used." For the results, did the times reported in the paper represent running the ESRNN-GPU implementation with multiple workers in aggregate (CPU Time), multiple workers concurrently (Wall Clock Time) or was the time reported for a single worker?

2. What GPU did you test on? Testing the M4 data set with CUDA enabled PyTorch on a notebook graphics cards (Nvidia GeForce GTX 1050) vs non-CUDA enabled PyTorch showed the CPU only version to be faster (i7 8550) by about 3x. It is likely that the PyTorch CPU enabled version is still faster the the original ESRNN, but I have not confirmed that.

3. Is there any plan to implemented the future work for Variable Length Series mentioned in Section 8.1? What would be required?

i have a problem in installing i type pip install git+https://github.com/damitkwr/ESRNN-GPU.git then error occurs then i type pip install git+https://github.com/damitkwr/ESRNN-GPU.git#egg=ESRNN-GPU then also error show plz help

Hello,

I've tried to install ESRNN via the instruction in this link: https://pypi.org/project/ESRNN/ which were: pip install ESRNN

However, when I try to run the follow code:

from ESRNN.m4_data import prepare_m4_data from ESRNN.utils_evaluation import evaluate_prediction_owa

from ESRNN import ESRNN

I get the following error:

Traceback (most recent call last): File "ESRNN.py", line 2, in from ESRNN.m4_data import prepare_m4_data File "C:\Users\mario\Documents\Python Benjamin\ESRNN.py", line 2, in from ESRNN.m4_data import prepare_m4_data ModuleNotFoundError: No module named 'ESRNN.m4_data'; 'ESRNN' is not a package

I've tried adding ESRNN as a path variable and still get the same error.

Could anyone please assist?

Hi, I am trying to use the code, but I encounter several issues. One is that, "train_path = '../data/Train/%s-train.csv' % (config['variable']) test_path = '../data/Test/%s-test.csv' % (config['variable'])" in main.py cause some problems. If I understand the code correctly, it changes the path to "../data/Train/Daily-train.csv", which is a CSV file that does not exist. The other issue is that I do not really understand what kind of information info.csv should contain. Would you please help me deal with these problems? Thanks

Edit: Never mind. The loss is just getting averaged as batch_num is outside of the for-loop that it increases in.

In trainer.py inside the train method after an epoch finishes, the epoch_loss is divided by the batch_num + 1. That means that after every batch, the epoch_loss is forcefully decreased as the denominatior (the batch_num) is constantly getting bigger:

epoch_loss = epoch_loss / (batch_num + 1)

Maybe I'm misunderstanding something here, but it doesn't seem right that the loss is getting artificially decreased simply based on which batch the training loop is on. I looked through the original C++ implementation, but couldn't find anything that looked like the above line (I don't know C++ very well, so that may be why).

P.S. Thanks for the python/torch implementation of this project btw, it's a great resource for learning some good forecasting methods/strategies.

Hey, thank you for publishing your results, very impressive. What is the csv formatting for Train and Test? I've noticed that read_file creates arrays of different shapes: Train ends up in shape (number_of_series, ) Test - (number_of_series, time_steps)

I would like to reproduce it on my data. How to format Train csv with pd.to_csv to be properly processed by your code?

Thanks! Best regards

This can append too many months to the seasonalities. In the case of 'Monthly' data, config['output_size'] is 18, config['seasonality'] is 12. If seasonalities_stacked.shape[1] is 72, start_seasonality_ext is 60, so line 89 appends 12 seasonalities, rather than 6 (as in the C++ dynet code).

 //if prediction horizon is larger than seasonality, so we need to repeat some of the seasonality factors
                if (OUTPUT_SIZE_I > SEASONALITY) {
                    unsigned long startSeasonalityIndx = season_exVect.size() - SEASONALITY;
                    for (int i = 0; i < (OUTPUT_SIZE_I - SEASONALITY); i++)
                        season_exVect.push_back(season_exVect[startSeasonalityIndx + i]);
                }

This becomes a problem on line 126 where we select the last config['output_size'], which will select the wrong output_size elements. In the case above, the seasonalities will be shifted by 6 months (18 - 12 = 6).

def read_file(C:\Users\welcome\Downloads\m4 forecast\M4-methods-master\Dataset()):

SyntaxError: unexpected character after line continuation character

Hi, I've tried to understand the dataset and how do you really train the model on it, but it seems that the information is not available about the competition anymore. Can you explain how the data is loaded for example from monthly Train.csv?

Sorry to bother you. I can't find which part in this code implement the prediction intervals(PI) function. Is this code just for point forecast(PF)? Thank you.

great code thanks may you clarify : will it work for multivariate time series prediction both regression and classification 1 where all values are continues values weight height age target 1 56 160 34 1.2 2 77 170 54 3.5 3 87 167 43 0.7 4 55 198 72 0.5 5 88 176 32 2.3

2 or even will it work for multivariate time series where values are mixture of continues and categorical values for example 2 dimensions have continues values and 3 dimensions are categorical values

color        weight     gender  height  age  target 

1 black 56 m 160 34 yes 2 white 77 f 170 54 no 3 yellow 87 m 167 43 yes 4 white 55 m 198 72 no 5 white 88 f 176 32 yes

Hi there, My dataset only have two column- Date and Price. Is it possible to let the input be the price and the output be the price as well. If so, how should I divide them into x_train,y_train, and x_test, y_test. Will the algorithm do it for me automatically? Or if I only have these two columns, I will not be able to use this algorithm?