# Time Series Dataset Survey for Forecasting with Deep Learning

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## Abstract

**:**

## 1. Introduction

- 1.
- We provide a cross-domain overview of existing publicly available time series forecasting datasets that have been used in research.
- 2.
- Furthermore, we analyze these datasets regarding their domain and provide file and data structure, as well as general statistical characteristics, and compare them quantitatively with each other by computing their similarity.
- 3.
- We provide an overview of all public time series forecasting datasets identified in this publication and facilitate easy access with a list of links to all these datasets.
- 4.
- Finally, we facilitate comparability in the time series forecasting research area by calculating a grouping of datasets using the aforementioned similarity measures.

## 2. Related Survey Publications

## 3. Methodology

#### 3.1. Paper Screening

- 1.
- As a first step, we performed a screening of papers found in the “Web of Science” [4] to identify publicly available time series forecasting datasets which are used in research. To ensure the impact of the datasets in research, we limited our choice to papers that had been cited at least ten times. This reduced the number of papers from over 1000 to 207 and ensured that relevant papers with common datasets were not excluded. Another goal was to identify datasets already used for deep learning. We achieved this by adding the constraint of “deep learning” to the search query. As a result, the following Web of Science query was used:

- 2.
- To address the fact that newly published papers had not had the chance yo acquire ten citations to date, papers from the last year, 2021, had a restriction of having at least five citations and a maximum of ten citations as other papers were already included by step one. Therefore, we used the same Web of Science query as before and extracted 43 new publications on 17 December 2021.
- 3.
- To widen the search for datasets used in academic publications, we further utilized the website “papers with code” [29]. Then, “papers with code” was used to ensure the inclusion of recent publications from conferences that were not listed on “Web of Science”. The “papers with code” website has a collection of publicly available datasets with the associated papers that have published their code and results on a dataset. Furthermore, the website ranks publications per dataset by the number of stars of their corresponding Github repositories. We filtered the datasets by the categories of “time series” and “forecasting” to collect the datasets with their corresponding publications. We selected the top 10 ranked publications if a dataset had more than ten publications, resulting in 43 additional publications with eight datasets. Then, we used these publications for additional screening of datasets to find public datasets not identified by the website “papers with code”.

#### 3.2. Fundamentals of Statistical Time Series Characteristics

## 4. Time Series Domains

## 5. Screening of Public Datasets

- 1.
- Our first condition was that the data must be publicly accessible and not hidden behind a particular sign-in, or only available on request, to give an overview of general publicly available datasets.
- 2.
- The dataset must be directly downloadable as files to ensure reproducibility. Datasets that can only be accessed through a web view or dashboard, where multiple parameters need to be selected, were not included.
- 3.
- Datasets would not be considered if the data was only available in a specific country or the website was not in English, to ensure consistent access to the datasets.

## 6. Comparison of Selected Datasets

- 1.
- The forecast value must be clearly defined in a paper or a dataset description.
- 2.
- The defined forecasting value should not be aggregated over a period of time or locations.
- 3.
- For comparability, the target must be a univariate time series.

#### 6.1. Comparison of Selected Datasets with MPdist

#### 6.2. Comparison of Selected Datasets with Statistical Characteristics

## 7. Categorize the Datasets

- 1.
**stationary/high PRV/low to medium AC**: This category is a time series that is stationary and has many repeating values which are not distributed in regular patterns or distributed in some regular patterns. Similar datasets could be found in cluster one.- 2.
**stationary/high PRV/high AC**: This category is a time series that is stationary and has many repeating values which are distributed in regular patterns. Similar datasets could be found in cluster two.- 3.
**stationary/low PRV/low AC**: This category is a time series that is stationary and has many unique values which are distributed in irregular patterns. Similar datasets could be found in cluster three.- 4.
**stationary/low PRV/high AC**: This category is a time series that is stationary and has many unique values which are distributed in regular patterns. We only identified the dataset with ID 18 in the outlier cluster. This indicates that this category does not naturally appear in datasets used in research. This could be caused by the multiple domains which are combined in that dataset.- 5.
**non stationary**: Due to the small number of datasets we identified which were not stationary, this category could not be used for comparison. It is possible that there were multiple additional clusters that we did not identify. Nevertheless, the work done in this paper could be an indicator that there are not many stationary time series datasets for forecasting used in publications.

## 8. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

MDPI | Multidisciplinary Digital Publishing Institute |

DOAJ | Directory of open access journals |

TLA | Three letter acronym |

LD | Linear dichroism |

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**Figure 2.**Visualization of the public dataset search process with the number of publications used for the review.

**Figure 3.**Domain distribution of the datasets which were identified while screening the “Web of Science” publication corpus.

**Figure 5.**Samples from all dataset from Table 6 with a maximum length of 100 k.

**Table 1.**This table compares how the extracted surveys identified in this paper analyzed time series datasets. The tick indicates if the corresponding publication met the condition of the column.

Paper | Datasets with References | Easy access to the datasets | Multiple Datasets | Cross-domain | Comparison of Datasets | Dataset Statistics | Dataset Analysis |
---|---|---|---|---|---|---|---|

Ahmed et al. [12] | |||||||

Aslam et al. [13] | ✓ | ✓ | ✓ | ✓ | |||

Chandra et al. [14] | ✓ | ✓ | ✓ | ||||

Chen et al. [15] | |||||||

Dikshit et al. [16] | |||||||

Ghalehkhondabi et al. [17] | |||||||

Lara-Benitez et al. [18] | ✓ | ✓ | ✓ | ✓ | ✓ | ||

Liu et al. [19] | |||||||

Mosavi et al. [20] | ✓ | ||||||

Sengupta et al. [21] | ✓ | ✓ | ✓ | ✓ | |||

Somu et al. [22] | |||||||

Sun and Scanlon [23] | |||||||

Wang et al. [24] | |||||||

Wei et al. [25] | ✓ | ||||||

Weiss et al. [26] | ✓ | ||||||

Zambrano et al. [27] | ✓ | ✓ |

Astro | Machine Sensor | Mortality rate | Fertility rate |

Physics Simulation | Supply Chain | Tourism | NLP |

Crime | Garbage/Waste Prediction | Gas Consumption | Mobile Network |

Network Security | Trend Forecasting | Yield Prediction | Machine Sensor |

Chemicals | Cloud Load | AD Exchange | Bike-sharing |

Non-linear Problems | Web Traffic |

**Table 3.**This Table presents all links to the shown datasets from Table 4. The web links can be used to retrieve the before-shown datasets.

**Table 4.**A general overview of the public datasets found through the paper screening of “Web of Science”and the “papers with code” as defined in Section 3.1. The coding of the column “Data Structure” column is defined in Table 5 with the underlying structure (FileDatastructure/DatasetDescription/Timestamp).

ID | Domain | Data Structure | File Format | # Data Points | # Dimensions | Time Interval | Paper |
---|---|---|---|---|---|---|---|

0 | Windspeed | (−/−/−) | csv | 105,119 | 51 | 5 min | [36] |

1 | Electricity | (−/−/−) | csv | 105,119 | 31 | 5 min | [36] |

2 | Air Quality | (+/+/+) | csv | 43,824 | 12 | 1 h | [37,38,39,40] |

3 | Electricity | (+/+/+) | csv | 2,075,259 | 8 | 1 min | [37,41,42,43] |

4 | Air Quality | (+/+/+) | xlsx | 9471 | 16 | 1 h | [37,44] |

5 | Air Quality | (+/+/+) | csv | 2,891,393 | 7 | 1 h | [38] |

6 | Traffic | (+/o/−) | txt | 3,997,413 | 11 | 1 h | [2,37,45,46] |

7 | Crime | (+/+/+) | csv | 2,678,959 | 15 | irregular | [47] |

8 | Weather | (+/+/+) | txt | 2764 | 24 | 15 min | [48] |

9 | Ozone Level | (+/o/+) | csv | 2536 | 74 | 1 h | [44] |

10 | Fertility | (+/+/+) | rda | 574 | 4 | 1 yr | [49] |

11 | Mortality | (+/+/+) | csv | 21,201 | 8 | 1 yr | [49] |

12 | Weather, Bike-Sharing | (+/+/+) | csv | 731 | 15 | 1 d | [50] |

13 | Weather, Bike-Sharing | (+/+/+) | csv | 17,379 | 16 | 1 h | [50] |

14 | Electricity, Weather | (+/+/+) | xlsx | 713 | 3 | 1 d | [48] |

15 | Weather | (+/+/+) | xlsx | 15,072 | 12 | 1 h | [48] |

16 | Machine Sensor | (−/o/−) | txt | - | - | 100 ms | [51] |

17 | AD Exchange Rate | (+/o/+) | csv | 9610 | 3 | 1 h | [51] |

18 | Multiple | (+/o/+) | csv | 69,561 | 3 | 5 min | [51] |

19 | Traffic | (+/o/+) | csv | 15,664 | 3 | 5 min | [51] |

20 | Cloud Load | (+/o/+) | csv | 67,740 | 3 | 5 min | [51] |

21 | Tweet Count | (+/o/+) | csv | 158,631 | 3 | 5 min | [51] |

22 | Synthetic | (+/+/−) | mat | - | - | [52] | |

23 | Electricity | (+/−/−) | txt | 140,256 | 370 | 15 min | [45,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,45] |

24 | Exchange Rate | (+/−/−) | txt | 7587 | 7 | 1 d | [53,54,57,58,64,65,66,67] |

25 | Traffic | (+/−/−) | txt | 17,543 | 861 | 1 h | [53,57,58,64,65,66,67] |

26 | Solar | (+/−/−) | txt | 52,559 | 136 | 10 min | [53,63,64,65] |

27 | Weather | (+/+/+) | csv | - | - | 1 min | [68] |

28 | Water Level | (+/+/+) | xlsx | 36,160 | 4 | 1 d | [69] |

29 | Air Quality | (+/+/+) | csv | 420,768 | 19 | 15 min | [62,70] |

30 | Air Quality | (+/+/+) | csv | 79,559 | 11 | 15 min | [50,71] |

31 | Crime | (+/+/+) | csv | 2,129,525 | 34 | 1 min | [47] |

32 | Chemicals | (+/+/+) | xlsx | 120,630 | 7 | 1 min | [72] |

33 | Multiple | (+/−/−) | txt | 71 | 110 | 1 M. | [18] |

34 | Multiple | (+/+/+) | txt | 167,562 | 3 | 1 yr, 1 q, 1 m | [18,73,74,75,76] |

35 | Traffic | (+/+/−) | xls | - | - | 1 d | [18,73] |

36 | Tourism | (+/−/−) | csv | 309 | 794 | 1 m 1 q | [18] |

37 | Web Traffic | (+/+/+) | csv | 290,126 | 804 | 1 d | [18,73] |

38 | Multiple | (+/o/+) | csv | 414 | 960 | 1 yr, 1 q, 1 m, 1 w, 1 d, 1 h | [2,18,45,46,73,74,75,76,77,78] |

39 | Machine Sensor | (+/+/+) | csv | 34,840 | 9 | 1 h, 1 m | [54,55,56,57,58,59,60,79,80,81] |

40 | Synthetic | (−/−/−) | pickle | - | - | [82] | |

41 | Electricity | (+/+/+) | csv | 4,055,880 | 6 | 5 min, 1 h | [2,45,53,54,63,77,53] |

42 | Weather | (+/+/+) | csv | 633,494,597 | 125 | 1 yr | [83,84] |

43 | Electricity | (+/−/+) | csv | 257,896 | 27 | 1 h | [85,86] |

44 | Trajectory | (+/+/+) | txt | 8,241,680 | 14 | 1 s | [87] |

45 | Wind | (+/+/−) | csv | 262,968 | 254 | hourly | [2,45] |

46 | Bike-Usage | (+/+/+) | csv | 52,584 | 5 | hourly | [77] |

47 | Electricity | (+/+/+) | csv | 48,048 | 16 | hourly | [80] |

48 | Illness | (+/+/+) | csv | 966 | 7 | weekly | [57,58,66,67] |

49 | Sales | (+/+/+) | csv | 1,058,297 | 9 | daily | [43] |

50 | Weather | (+/+/+) | csv | 52,696 | 21 | 10 min | [57,58,66,67,81] |

51 | Traffic | (+/−/−) | mat | 57,636 | 48 | hourly | [43] |

52 | Weather | (+/+/+) | csv | 35,064 | 12 | hourly | [60,64] |

**Table 5.**Description of the column “Data Structure” with (FileDatastructure/DatasetDescription/Timestamp) from Table 4.

File Data Structure | Dataset Description | Timestamp | |
---|---|---|---|

+ | One file or multiple files with a clear structure and documentation. | The dataset contains a description of every field, which could lead to an understanding of all fields. | It is a timestamp, date or any date-related column defined. |

o | The dataset contains different placeholders in the data, which are explained later in a description. | ||

− | Multiple files in different directories without any obvious order and relation between each other. | There is no field description or an incomplete one. | There is no timestamp, date or any date-related column defined. |

ID | Time Interval | Domain | # Data Points | # Dimensions | Forecasting Value | ADF | AC | PRV |
---|---|---|---|---|---|---|---|---|

2 | 1 h | Air Quality | 43,824 | 12 | pm2.5 | 0.0000 | 0.4332 | 0.8795 |

3 | 1 min | Electricity | 2,075,259 | 8 | global_active_power | 0.0000 | 0.7028 | 0.9088 |

4 | 1 h | Air Quality | 9471 | 16 | pt08.s1(co) | 0.0000 | 0.4333 | 0.8714 |

5 | 1 h | Air Quality | 2,891,393 | 7 | pm25_concentration | 0.0000 | 0.3299 | 0.9154 |

10 | 1 yr | Fertility | 574 | 4 | fert-female | 0.2438 | 0.3113 | 0.2000 |

11 | 1 yr | Mortality | 21,201 | 8 | mort-female | 0.0179 | 0.0851 | 0.0896 |

12 | 1 d | Weather, Bike-Sharing | 731 | 15 | cnt | 0.3427 | 0.6827 | 0.0503 |

13 | 1 h | Weather, Bike-Sharing | 17,379 | 16 | cnt | 0.0000 | 0.0963 | 0.8872 |

17 | 1 h | AD Exchange Rate | 9610 | 3 | value | 0.0032 | 0.0085 | 0.0000 |

18 | 5 min | Multiple | 69,561 | 3 | value | 0.0000 | 0.9195 | 0.0000 |

19 | 5 min | Traffic | 15,664 | 3 | value | 0.0000 | 0.0852 | 0.5500 |

20 | 5 min | Cloud Load | 67,740 | 3 | value | 0.0000 | 0.0496 | 0.0584 |

21 | 5 min | Tweet Count | 158,631 | 3 | value | 0.0000 | 0.1992 | 0.7619 |

28 | 1 d | Water Level | 17,543 | 861 | dailyrunoff | 0.0000 | 0.1435 | 0.8597 |

29 | 15 min | Air Quality | 52,559 | 136 | pm2.5 | 0.0000 | 0.8577 | 0.7647 |

30 | 15 min | Air Quality | 79,559 | 11 | value | 0.0000 | 0.5948 | 0.8852 |

39 | 1 h | Machine Sensor | 34,840 | 9 | ot | 0.0052 | 0.5594 | 0.8544 |

41 | 5 min, 1 h | Electricity | 4,634,040 | 6 | power(mw) | 0.0000 | 0.6134 | 0.9773 |

49 | 1 d | Sales | 1,058,297 | 9 | sales | 0.0000 | 0.2742 | 0.8488 |

52 | 1 h | Weather | 35,064 | 12 | wetbulbcelsius | 0.0000 | 0.7422 | 0.9557 |

**Table 7.**Overview of the identified clusters and the corresponding statistical features of the dataset.

ID | Domain | Time Interval | # Data Points | # Dimensions | ADF | AC | PRV |
---|---|---|---|---|---|---|---|

Outliers | |||||||

10 | Fertility | 1 yr | 574 | 4 | 0.2438 | 0.3113 | 0.2000 |

12 | Weather, Bike-Sharing | 1 d | 731 | 15 | 0.3427 | 0.6827 | 0.0503 |

18 | Multiple | 5 min | 69,561 | 3 | 0.0000 | 0.9195 | 0.0000 |

19 | Traffic | 5 min | 15,664 | 3 | 0.0000 | 0.0852 | 0.5500 |

29 | Air Quality | 15 min | 420,768 | 19 | 0.0000 | 0.8577 | 0.7647 |

Cluster 1 | |||||||

2 | Air Quality | 1 h | 43,824 | 12 | 0.0000 | 0.4332 | 0.8795 |

4 | Air Quality | 1 h | 9471 | 16 | 0.0000 | 0.4333 | 0.8714 |

5 | Air Quality | 1 h | 2,891,393 | 7 | 0.0000 | 0.3299 | 0.9154 |

13 | Weather, Bike-Sharing | 1 h | 17,379 | 16 | 0.0000 | 0.0963 | 0.8872 |

21 | Tweet Count | 5 min | 158,631 | 3 | 0.0000 | 0.1992 | 0.7619 |

28 | Water Level | 1 d | 36,160 | 4 | 0.0000 | 0.1435 | 0.8597 |

39 | Machine Sensor | 1 h, 1 m | 34,840 | 9 | 0.0052 | 0.5594 | 0.8544 |

49 | Sales | 1 d | 1058297 | 9 | 0.0000 | 0.2742 | 0.8488 |

Cluster 2 | |||||||

3 | Electricity | 1 min | 2,075,259 | 8 | 0.0000 | 0.7028 | 0.9088 |

30 | Air Quality | 15 min | 79,559 | 11 | 0.0000 | 0.5948 | 0.8852 |

41 | Electricity | 5 min, 1 h | 4,826,760 | 6 | 0.0000 | 0.6134 | 0.9773 |

52 | Weather | 1 h | 35064 | 12 | 0.0000 | 0.7422 | 0.9557 |

Cluster 3 | |||||||

11 | Mortality | 1 yr | 21,201 | 8 | 0.0179 | 0.0851 | 0.0896 |

17 | AD Exchange Rate | 1 h | 9610 | 3 | 0.0032 | 0.0085 | 0.0000 |

20 | Cloud Load | 5 min | 67,740 | 3 | 0.0000 | 0.0496 | 0.0584 |

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## Share and Cite

**MDPI and ACS Style**

Hahn, Y.; Langer, T.; Meyes, R.; Meisen, T.
Time Series Dataset Survey for Forecasting with Deep Learning. *Forecasting* **2023**, *5*, 315-335.
https://doi.org/10.3390/forecast5010017

**AMA Style**

Hahn Y, Langer T, Meyes R, Meisen T.
Time Series Dataset Survey for Forecasting with Deep Learning. *Forecasting*. 2023; 5(1):315-335.
https://doi.org/10.3390/forecast5010017

**Chicago/Turabian Style**

Hahn, Yannik, Tristan Langer, Richard Meyes, and Tobias Meisen.
2023. "Time Series Dataset Survey for Forecasting with Deep Learning" *Forecasting* 5, no. 1: 315-335.
https://doi.org/10.3390/forecast5010017