This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

Rakshitha Godahewa

Last update: Dec 30, 2022

Related tags

Overview

TSForecasting

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

The benchmark datasets are available at: https://zenodo.org/communities/forecasting. For more details, please refer to our website: https://forecastingdata.org/ and paper: https://arxiv.org/abs/2105.06643.

All datasets contain univariate time series and they are availble in a new format that we name as .tsf, pioneered by the sktime .ts format. The data can be loaded into the R environment in tsibble format [1] by following the example in "utils/data_loader.R". It uses a similar approach to the arff file loading method in R foreign package [2]. The data can be loaded into the Python environment as a Pandas dataframe by following the example in "utils/data_loader.py". Download the .tsf files as required from our Zenodo dataset repository and put them into "tsf_data" folder.

The fixed horizon, rolling origin and feature calculation related experiments are there in the "experiments" folder. Please see the examples in the corresponding R scripts in the "experiments" folder for more details. Makesure to create a folder named "results" in the parent level and sub-folders as necessary before running the experiments. The outputs of the experiments will be stored into the sub-folders within the "results" folder as mentioned follows:

Sub-folder Name	Stored Output
rolling_origin_forecasts	rolling origin forecasts
rolling_origin_errors	rolling origin errors
rolling_origin_execution_times	rolling origin execution times
fixed_horizon_forecasts	fixed horizon forecasts
fixed_horizon_errors	fixed horizon errors
fixed_horizon_execution_times	fixed horizon execution times
tsfeatures	tsfeatures
catch22_features	catch22 features
lambdas	boxcox lambdas

Citing Our Work

When using this repository, please cite:

@misc{godahewa2021monash,
    author="Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo",
    title="Monash Time Series Forecasting Archive",
    howpublished ="\url{https://arxiv.org/abs/2105.06643}",
    year="2021"
}

References

[1] Wang, E., Cook, D., Hyndman, R. J. (2020). A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational and Graphical Statistics. doi:10.1080/10618600.2019.1695624.

[2] R Core Team (2018). foreign: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', .... R package version 0.8-71. https://CRAN.R-project.org/package=foreign

Comments

sktime integration of `data_loader.convert_tsf_to_dataframe`

Really nice collection of forecasting benchmark datasets you have here!

We were wondering (at sktime) whether you would be open for us to integrate (a possibly modified) data_loader.convert_tsf_to_dataframe into the data_io module of sktime, using the Monash forecasting repository as a data endpoint. Of course with proper attribution and crediting of the source.

Since tsf is based on the ts format and the loaders are similar, it would fit nicely with a current refactoring effort in the space.

If you'd be up for a chat and/or collaboration, feel free to visit us on the sktime slack, forecasting or forecasting-global channel. (go https://github.com/alan-turing-institute/sktime, README -> slack badge at the top) Might also be nice to collaborate on "nice" benchmark functionality, which can be loaded as a package and which directly interfaces with existing base class templates (with no need to write extra glue code that's special to the benchmark)

opened by fkiraly 5
covariate and forecasting target

Hello! I had a question regarding the forecasting target vs. additional covariates in the dataset. For example, in the temperature_rain dataset there are some fields that I assume are the forecast target from the names of the column while others are covariates... How does the tst format distinguish between those?

opened by kashif 4
Unable to reproduce the results from paper

Hi,

Monash Forecasting Repository and your work is greatly appreciated. Thanks a lot for making the work reproducible.

However, I tried experimenting with a few datasets and found that I was unable to reproduce the same results for the local models like ARIMA, ETS, SES, etc. Here are the results that I found for the COVID dataset. I use the same script and the same lag and horizon values. Could you please let me know if I am doing something wrong?. Should I change some parameters for these local models in order to attain the results reported in the paper?.

These are the results I got for the COVID dataset.

| ETS | TBATS | SES | ARIMA -- | -- | -- | -- | -- MyExperiment | 8.98 | 8.98 | 8.977 | 6.104 Published Results | 5.33 | 5.72 | 7.776 | 6.117

Additionally, I also found that TBATS, ETS, and SES mostly always show the same error. Do you have an idea about why this could be true for the COVID dataset?

opened by 18kiran12 3
corrected import of sktime

The package structure of sktime changed a bit in the current version, so the import wasn't working for me and this PR corrects it. Here's a related issue: https://github.com/sktime/sktime-dl/issues/79

opened by timoschowski 3
Data leakage for global univariate setting?

Hi, thanks for your amazing work! One issue I'm wondering about is, what constitutes a data leak in the global univariate setting?

Please correct me if I'm wrong, but I noticed that for the global univariate settings where the time series is unaligned (specifically, an unaligned end date), this leads to a shorter time series X to have test set in the same period as a longer time series Y's training set. (Examples would include Vehicle Trips and Tourism datasets).

There could possibly be some leakage of global information (e.g. in some financial time series there could be some systematic movement of all assets), leading to unfair advantage for global models.

opened by gorold 2
Default trainer from Gluon package in the deep_learning_experiments script

Hello, thank you for making this benchmark available. I noticed that the deep learning implementation relies on the Gluon package and in the train_model function, the trainer is always used with the defaulted parameters. I wonder if it is not better to adjust some params to the considered dataset (basically the num_batches_per_epoch parameter).

opened by BoudabousSafa 2

Why is arima giving an error when I use a subset of NN5 dataset without missing values?

Here is the procedure I followed:

Download nn5_daily_dataset_without_missing_values.tsf from Zenodo.
Remove all the time series except the first and second one from TSF file and save as nn5_small.tsf. In the resulting TSF file there are two time series T1 and T2.
Run "do_fixed_horizon_local_forecasting("nn5_small", "arima", "nn5_small.tsf","series_name", "start_timestamp")"

When I follow the above steps, I get the following error:

> do_fixed_horizon_local_forecasting("nn5_small", "arima", "nn5_small.tsf","series_name", "start_timestamp")
[1] "Started loading nn5_small"
[1] "started Forecasting"
[1] 1
[1] 2
[1] "Finished Forecasting"
Time difference of 1.184725 mins
The length of the provided data differs.
Length of holdout: 56
Length of forecast: 0
Error: Cannot proceed.
In addition: Warning messages:
1: The chosen seasonal unit root test encountered an error when testing for the first difference.
From stl(): NA/NaN/Inf in foreign function call (arg 1)
0 seasonal differences will be used. Consider using a different unit root test. 
2: The chosen seasonal unit root test encountered an error when testing for the first difference.
From stl(): NA/NaN/Inf in foreign function call (arg 1)
0 seasonal differences will be used. Consider using a different unit root test. 
3: In array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x),  : 'data' must be of a vector type, was 'NULL'

When I check the result file, I realized that there is no forecast for T1.

$ cat results/fixed_horizon_forecasts/nn5_small_arima.txt 
T1
T2,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771,12.9393424036281,15.3486394557823,18.75,29.6768707482993,30.7256235827664,16.7375283446712,12.046485260771

All other five methods (ets,ses,theta, tbats, and dhr_arima) work fine.

I wonder if this is an expected behaviour?

opened by hkayabilisim 2

Missing documentation in the notebook

In the forecastingdata_python notebook, it should be nice if you could add something like this:

After a fresh R installation, you need to install devtools by running "install.packages("devtools")" in R command line.

By the way, thank you for this great forecasting repository and the tools you created in the GitHub repo.

opened by hkayabilisim 2
Notebook for features and fixed horizon
Some highlights:

refactor the experiments into functions and experiment files

added recursive path creation

added removing existing file (always appending makes the test fail when run after the first time)

added tryCatch for checking it catboost is installed, because it is difficult to install
opened by pmontman 1
License

Hi,

Thanks for making this data repository available! Could you please add a license for the code (e.g., the data loader)?

From GitHub docs:

without a license, the default copyright laws apply, meaning that you retain all rights to your source code and no one may reproduce, distribute, or create derivative works from your work.

Thank you, Alex

opened by aldro61 1

UnicodeDecodeError in data_loader.py when reading m4_monthly_dataset.tsf on Windows

I downloaded and extracted M4 monthly dataset and startet data_loader.py. I get following error message:

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-3-78dfe61da181> in <module>
      1 filename = 'm4_monthly_dataset.tsf'
      2 loaded_data, frequency, forecast_horizon, contain_missing_values, contain_equal_length = \
----> 3     data_loader.convert_tsf_to_dataframe("tsf_data/"+filename)
      4 
      5 print('loaded_data',loaded_data)

~\Documents\PythonScripts\Timeseries2020\MonashTSForecastingArchiv\data_loader.py in convert_tsf_to_dataframe(full_file_path_and_name, replace_missing_vals_with, value_column_name)
     28 
     29     with open(full_file_path_and_name, 'r', encoding='utf-8') as file:
---> 30         for line in file:
     31             # Strip white space from start/end of line
     32             line = line.strip()

~\miniconda3\envs\sktime\lib\codecs.py in decode(self, input, final)
    320         # decode input (taking the buffer into account)
    321         data = self.buffer + input
--> 322         (result, consumed) = self._buffer_decode(data, self.errors, final)
    323         # keep undecoded input until the next call
    324         self.buffer = data[consumed:]

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 437: invalid start byte

If I change encoding from utf-8 to ansi it works:

 #with open(full_file_path_and_name, 'r', encoding='utf-8') as file:
 with open(full_file_path_and_name, 'r', encoding='ansi') as file:

I work on Windows 10 with Python 3.9.4

opened by JBOE22175 1

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

Related tags

Overview

TSForecasting

Citing Our Work

References

Comments

sktime integration of `data_loader.convert_tsf_to_dataframe`

covariate and forecasting target

Unable to reproduce the results from paper

corrected import of sktime

Data leakage for global univariate setting?

Default trainer from Gluon package in the deep_learning_experiments script

Why is arima giving an error when I use a subset of NN5 dataset without missing values?

Missing documentation in the notebook

Notebook for features and fixed horizon

License

UnicodeDecodeError in data_loader.py when reading m4_monthly_dataset.tsf on Windows

Owner

Rakshitha Godahewa

Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“.

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Time Series Forecasting with Temporal Fusion Transformer in Pytorch

A small library of 3D related utilities used in my research.

Automatically download the cwru data set, and then divide it into training data set and test data set

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Event-forecasting - Event Forecasting Algorithms With Python

Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).

Python-experiments - A Repository which contains python scripts to automate things and make your life easier with python

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"