The winners of the very first round of the European statistics awards on nowcasting
We are pleased to announce the winners of the accuracy and reproducibility awards of the challenges on Producer prices in industry (PPI), Production volume in industry (PVI) and Tourism (Number of nights spent at tourism accommodation establishments).
Since 2022, Eurostat has conducted engaging competitions in the fields of nowcasting and web intelligence, with the primary objective of unveiling innovative methodologies and valuable data resources to enhance the production of European statistics.
As part of this program, which will continue until the end of 2025, the initial rounds of the nowcasting and web intelligence competitions have concluded.
We thank all participants for having contributed to the success of this first round through their submissions. Moving forward, we aim to leverage insights gained from this experience to enhance future rounds. We encourage the community of data-savvy enthusiasts to consider participating in upcoming competitions to be announced in the coming months. Stay tuned for further details and assist us in generating timely and detailed European statistics.
Congratulations to all the winners!
Nowcasting – 1st round – Producer prices in industry, Production volume in industry, Tourism Challenge
The European Statistics Awards Programme on Nowcasting aims at improving timelines of European Statistics which is a recurring demand by policymakers and other users of European statistics.
We were looking for new approaches based on advanced modelling (possibly using alternative, almost real-time, information) that could give us accurate estimates of key indicators much faster than before.
The 2022 round of the Nowcasting awards was divided into 3 separate competitions, each focusing on the nowcasting of one economic indicator.
Producer prices in industry – PPI Challenge
Producer prices are also known as output prices. The objective of the output price index is to measure the monthly development of transaction prices of economic activities. There is a general public interest in knowing the extent to which the prices of goods and services have risen. In many countries levels of wages, pensions, and payments are adjusted in long term contracts in proportion to changes in relevant prices. The domestic output price index for an economic activity measures the average price development of all goods and related services resulting from that activity and sold on the domestic market between one time period and another.
Production volume in industry – PVI Challenge
The monthly PVI indicator represents the production index in industry. The objective of the index is to measure changes in the volume of output at monthly intervals. It provides a measure of the volume trend in value added over a given reference period. The production index is calculated in the form of a Laspeyres type index.
Number of nights spent at tourism accommodation establishments – Tourism Challenge
Accommodation statistics are a key part of the system of tourism statistics in the European Union and have a long history of data collection. For this indicator, the benchmark was the official figures on nights spent at tourist accommodation establishments by residents and non-residents.
As part of the competition requirements, teams were expected to submit at least 6 consecutive point estimates of monthly time series in a period of eight months across a minimum of five countries. The submission of estimates for all eight months (and more than five countries) enhanced the team's chances of securing the top position for the accuracy award. Teams competing for the Reproducibility Award were required to provide comprehensive methodological descriptions, including source code.
The Tourism, PVI and PPI challenge was launched in September 2022, with a final deadline for submissions in March 2023 and documentation in April 2023.
Participation was quite wide as a total of 82 teams, comprised of 144 individuals from 21 countries, signed up for this challenge. The results of the evaluation are announced below.
The participants were competing for two types of awards:
- Accuracy Award prizes were given to the top three teams whose entries yielded point estimates closest to the published tourism indicator.
- Reproducibility Award prize was given to the team whose entry shows great potential to be scaled up to European statistics production. The Reproducibility Award was intended to support the best described and documented solutions, with the most innovative, open approach using open data.
Accuracy Award winners - PPI (Producer prices in industry) Challenge
| Place | Prize | Team name | Team members | Country | Winning entry |
|---|---|---|---|---|---|
| 1st place | 3 000 EUR | nbb_dsc_cl | Corentin Lemasson | Belgium | Entry 3 |
| 2nd place | 2 000 EUR | dhopp1 | Daniel Hopp | Switzerland | Entry 1 |
| 3rd place | 1 000 EUR | INSEE |
Thomas Faria Pierre Leblanc Inès Moutachaker Antoine Palazzolo Alain Quartier la Tente |
France | Entry 1 |
Accuracy Award winners - PVI (Production volume in industry) Challenge
| Place | Prize | Team name | Team members | Country | Winning entry |
|---|---|---|---|---|---|
| 1st place | 3 000 EUR | GladGan | Christian Url | Austria | Entry 1 |
| 2nd place | 2 000 EUR | YXAnalys | Yingfu Xie | Sweden | Entry 1 |
| 3rd place | 1 000 EUR | INSEE |
Thomas Faria Pierre Leblanc Inès Moutachaker Antoine Palazzolo Alain Quartier la Tente |
France | Entry 1 |
Accuracy Award winners - Tourism (Number of nights spent at tourism accommodation establishments) Challenge
| Place | Prize | Team name | Team members | Country | Winning entry |
|---|---|---|---|---|---|
| 1st place | 3 000 EUR | PM10 |
Daniel Strenger Maximilian Ofner |
Austria | Entry 2 |
| 2nd place | 2 000 EUR | bernese |
Sarah Schneeberger Marc Burri |
Switzerland | Entry 4 |
| 3rd place | 1 000 EUR | dhopp1 | Daniel Hopp | Switzerland | Entry 1 |
Reproducibility Award winners
| Competition | Prize | Team name | Team members | Country | Winning entry |
|---|---|---|---|---|---|
| PPI | 5 000 EUR | dhopp1 | Daniel Hopp | Switzerland | Entry 1 |
| PVI | 5 000 EUR | Not attributed | - | - | - |
| Tourism | 5 000 EUR | bernese |
Sarah Schneeberger Marc Burri |
Switzerland | Entry 4 |
Solutions of the Reproducibility Award winners
Since the Reproducibility award was intended to support the most thoroughly described and documented solutions, with the most innovative, open approach, we are happy to share with you the solutions which won this prize in three competitions (PPI, PVI and Tourism):
PPI (Producer prices in industry)
dhopp1 solution which won the Reproducibility award for presenting the best solution for the indicator Producer prices in industry is available on the following link:
Tourism (Number of nights spent at tourist accommodation establishments)
Bernese solution which won the Reproducibility award for presenting the best solution for the indicator Number of nights spent at tourist accommodation establishments is available on the following link:
Final Performance Rankings
PPI (Producer prices in industry) Challenge
| Rank | Team name | Entry 1 score | Entry 2 score | Entry 3 score | Entry 4 score | Entry 5 score | Best entry score |
|---|---|---|---|---|---|---|---|
| 1 | nbb_dsc_cl | 0.005285 | 0.006557 | 0.005045 | 0.005045 | ||
| 2 | dhopp1 | 0.005157 | 0.005247 | 0.005469 | 0.005157 | ||
| 3 | INSEE | 0.005897 | 0.009513 | 0.008284 | 0.007515 | 0.005897 | |
| 4 | YXAnalys | 0.005908 | 0.014192 | 0.020289 | 0.032047 | 0.051172 | 0.005908 |
| 5 | Corsbu | 0.008139 | 0.007222 | 0.007222 | |||
| 6 | bernese | 0.010968 | 0.008395 | 0.011742 | 0.007993 | 0.010056 | 0.007993 |
| 7 | mb | 0.010301 | 0.008097 | 0.008426 | 0.014622 | 0.008231 | 0.008097 |
| 8 | Spika | 0.013085 | 0.010383 | 0.014846 | 0.012557 | 0.009752 | 0.009752 |
| 9 | Ronzinante | 0.009847 | 0.013346 | 0.013346 | 0.013346 | 0.013346 | 0.009847 |
| 10 | mustafaaskin | 0.010603 | 0.020932 | 0.010603 | |||
| 11 | Fantastic_Three | 0.013900 | 0.013900 | ||||
| 12 | GladGan | 0.019161 | 0.021170 | 0.019161 | |||
| 13 | AdrianB | 0.057916 | 0.088863 | 0.057916 | |||
| - | teamcof | These teams do have the same min 5 countries for 6 consecutive months, but have some values where MSRE is greater than the set threshold = 0.15. | |||||
| - | DreamTeamCy | ||||||
| - | outcasted_outliers | ||||||
| - | Koala | ||||||
| - | HDProjections | These teams did not provide the same min 5 countries for 6 consecutive months. | |||||
| - | HablandoEnData | ||||||
| - | Jupyter | ||||||
| - | MDPEI | ||||||
| - | Modil | ||||||
| - | Random_walk | ||||||
| - | SEER-Group | ||||||
| - | Statistical_Information_Entropy | ||||||
| - | TeamACJN | ||||||
| - | delphi | ||||||
| - | tsLovers | ||||||
| - | vega | ||||||
| - | vishleshak | ||||||
PVI (Production volume in industry) Challenge
| Rank | Team name | Entry 1 score | Entry 2 score | Entry 3 score | Entry 4 score | Entry 5 score | Best entry score |
|---|---|---|---|---|---|---|---|
| 1 | GladGan | 0.004725 | 0.006976 | 0.004725 | |||
| 2 | YXAnalys | 0.005799 | 0.006433 | 0.005799 | |||
| 3 | INSEE | 0.006154 | 0.007345 | 0.008739 | 0.009512 | 0.007905 | 0.006154 |
| 4 | mb | 0.014128 | 0.007578 | 0.007751 | 0.018603 | 0.007236 | 0.007236 |
| 5 | bernese | 0.007880 | 0.009284 | 0.010417 | 0.007703 | 0.010094 | 0.007703 |
| 6 | Jupyter | 0.007849 | 0.007849 | ||||
| 7 | Ronzinante | 0.008105 | 0.008105 | 0.008105 | 0.008105 | 0.008105 | 0.008105 |
| 8 | dhopp1 | 0.009105 | 0.009497 | 0.008539 | 0.008539 | ||
| 9 | mustafaaskin | 0.009057 | 0.013762 | 0.009057 | |||
| 10 | Spika | 0.010621 | 0.012983 | 0.011899 | 0.012796 | 0.010948 | 0.010621 |
| 11 | Fantastic_Three | 0.011055 | 0.011055 | ||||
| 12 | DreamTeamCy | 0.011341 | 0.011341 | ||||
| 13 | Koala | 0.014415 | 0.014415 | ||||
| 14 | outcasted_outliers | 0.015743 | 0.015743 | ||||
| 15 | nbb_dsc_cl | 0.022039 | 0.022039 | ||||
| 16 | Corsbu | 0.062170 | 0.041505 | 0.041505 | |||
| - | AdrianB | These teams did not provide the same min 5 countries for 6 consecutive months. | |||||
| - | HablandoEnData | ||||||
| - | JANE | ||||||
| - | MDPEI | ||||||
| - | Modil | ||||||
| - | SEER-Group | ||||||
| - | TeamACJN | ||||||
| - | tsLovers | ||||||
| - | vishleshak | ||||||
Tourism (Number of nights spent at tourism accommodation establishments) Challenge
| Rank | Team name | Entry 1 score | Entry 2 score | Entry 3 score | Entry 4 score | Entry 5 score | Best entry score |
|---|---|---|---|---|---|---|---|
| 1 | PM10 | 0.062206 | 0.052548 | 0.052548 | |||
| 2 | bernese | 0.064254 | 0.059753 | 0.061466 | 0.052800 | 0.088140 | 0.052800 |
| 3 | dhopp1 | 0.084791 | 0.114550 | 0.093104 | 0.084791 | ||
| 4 | OptimalEffort | 0.663310 | 1.708523 | 0.460193 | 1.474072 | 0.130610 | 0.130610 |
| 5 | GladGan | 0.158459 | 0.266043 | 0.391566 | 0.158459 | ||
| 6 | INSEE | 0.244560 | 0.218528 | 0.314124 | 0.317296 | 0.218528 | |
| 7 | YXAnalys | 0.296623 | 0.328733 | 0.296623 | |||
| 8 | Modil | 0.336400 | 0.336400 | ||||
| 9 | Fantastic_Three | 0.455392 | 0.455392 | ||||
| 10 | mustafaaskin | 1.107572 | 0.872781 | 0.872781 | |||
| 11 | Ronzinante | 1.102195 | 1.102195 | 1.102195 | 1.102195 | 1.102195 | 1.102195 |
| 12 | Koala | 1.396423 | 1.396423 | ||||
| 13 | Jupyter | 1.984225 | 1.984225 | ||||
| - | INESIS | These teams do have the same min 5 countries for 6 consecutive months, but have some values where MSRE is greater than the set threshold = 0.15. | |||||
| - | Corsbu | ||||||
| - | DreamTeamCy | ||||||
| - | HETS | These teams did not provide the same min 5 countries for 6 consecutive months. | |||||
| - | MDPEI | ||||||
| - | Quasar | ||||||
| - | Rozinante | ||||||
| - | SEER-Group | ||||||
| - | TeamACJN | ||||||
| - | dephi | ||||||
| - | mb | ||||||
| - | tsLovers | ||||||
| - | vishleshak | ||||||