{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# The State of Time Series Forecasting in Kaggle Competitions\n\n","metadata":{}},{"cell_type":"markdown","source":"# Overview\n\n•\t**Category**: Tabular and/or time series data  <br> \n•\tHistorical evolution of forecasting competitions <br> \n•\tChanging nature of forecasting problems, and the role of Kaggle  <br> \n•\tReview of recent Kaggle time series competitions <br> \n•\tLearnings from Kaggle competitions and emerging issues in forecasting <br> \n","metadata":{}},{"cell_type":"markdown","source":"# Introduction\n \nTime series refers to a collection of data points or observations recorded at specific time intervals. Since ancient times, human beings have sought to predict the future. Civilizations such as the Greeks, Romans, and Mayans observed cyclic patterns in weather, agriculture, and astronomy to forecast forthcoming events. These historical records, meticulously maintained by the Mayan priesthood, gave rise to the concept of time series data (Rice, 2008). By arranging data points in chronological order, analyzing time series data is an ongoing pursuit to derive meaningful statistical insights about the future. Its significance has been rapidly increasing due to its ability to provide a historical context that aids in forecasting investment trends, facilitating faster business innovations, and enabling better-informed decision-making across various domains. Additionally, time series databases represent the fastest-growing category among databases. The past decade has seen an increase in the proposed models corresponding to the significant rise in high data volumes (Alsharef et al., 2022; Shen et al., 2020).\n\nThis paper focuses on recent advancements in time series forecasting and the role of the Kaggle platform in the era of big data. Time series analysis has witnessed significant advances, expanding the applications of time series data beyond expectations. Currently, time series analysis finds application in various domains such as financial analysis, economic forecasting, sales forecasting, weather forecasting, and more. This paper examines the advancements in time series analysis technologies and explores the corresponding changes observed in Kaggle competitions in the past couple of years.\n\nThis essay is organized as follows. In the second section, we provide background about the critical role of forecasting competitions in developing and testing time-series methods and how Kaggle could make such competitions more diverse in terms of problems, data, methods, and people. Then, we describe our approach and present a descriptive analysis of the recent Kaggle time-series competitions. The following section provides a detailed summary of the findings of the recent competitions conducted on Kaggle involving time series data. In the next section, we reconcile our findings with our initial questions and raise a few critical issues about the direction of research on time-series forecasting and the role the Kaggle community may play as its vanguard.\n\n","metadata":{}},{"cell_type":"markdown","source":"# Role of Forecasting Competitions in Time-Series\n\nThe idea of learning from the wisdom of the masses through a forecasting competition was formally elucidated in an influential article by Galton (1907) in Nature. In this pioneering competition, the organizer asked people to predict the meat yield from slaughtering and dressing an ox at the annual show of the West of England Fat Stock and Poultry Exhibition at Plymouth. The participants included people consisting of experts in the meat industry as well as laypersons. Due care was taken to eliminate any non-serious entries through a participation fee. The middlemost entry out of 787 valid entries, aptly termed *Vox Populi* (voice of the people), proved to be within 1% of the actual meat yield. According to the author, the findings lent credence to the reliability of the democratic judgment of the masses.\n\nFast forward to the present day, the idea of the collective wisdom of experts has become quite entrenched in the field of time-series forecasting. By the end of the 20th century, the practice of survey forecasts became the benchmark for comparison of many economic and financial time series. These surveys of forecasters often ask leading experts their opinion on critical macroeconomic variables of interest, such as GDP, unemployment, inflation, etc. Some examples of survey forecasts are the quarterly Survey of Professional Forecasters (SPF) issued by the Federal Reserve Bank of Philadelphia, Blue Chip Economic Indicators, and the survey by Consensus Economics. These surveys typically interview leading economists to obtain their latest forecasts and opinions. Analysts use different methods and computing tools and might have various optimistic or pessimistic biases. Yet, the consensus forecast, usually a measure of central tendency, is often accepted as the experts' opinion about the economy.\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"### *Early M-Competitions*\n\nForecasting competitions played an important role in the development and testing of forecasting methods, none more than the M-competitions held by Spyros Makridakis and others. In their seminal work, Makridakis and Hibon (1979) compared the main methods available at that time to forecast 111 time series. Their finding that simple methods tend to perform better than more complex methods like ARMA challenged the established wisdom in the field of forecasting at that time. This paper created a stir among the forecasting community, and the authors followed up in 1982 with a forecasting competition to address some of the concerns raised about their 1979 study. Known as the M1-competition, this is the first true competition in time-series forecasting with the participation of seven experts that used 24 methods to forecast upto 1001 series (Makridakis et al., 1982).  The findings suggested that forecast combinations outperform individual models for the selected time-series. This result contradicted the prevailing belief at the time that there existed an underlying behind the data generating process, and forecasters were supposed to identify the models. These were revolutionary ideas at that time, and the noise generated around the results indicated the need for additional competitions. The M2-competition attempted to determine post-sample accuracy of the models and allowed the forecasters to incorporate additional judgmental information (Makridakis et al., 1993). However, the results did not show any considerable improvement over automated methods, and simple models based on exponential smoothing were found to outperform complex models (Makridakis et al., 1993). The next iteration of the competition consisted of 3003 time-series from mainly business, demography, and economics, and had 24 participants. At the conclusion of this competition, the ideas originating from the earlier iterations seemed to be validated. We reproduce, in verbatim, the four conclusions from M-competitions until that point (Makridakis & Hibon, 2000):  \n\n*1. “Statistically sophisticated or complex methods do not necessarily produce more accurate forecasts than simpler ones.”* <br>\n*2. “The rankings of the performance of the various methods vary according to the accuracy measure being used.”* <br>\n*3. “The accuracy of the combination of various methods outperforms, on average, the specific methods being combined and does well in comparison with other methods.”* <br>\n*4. “The performance of various methods depends upon the length of the forecasting horizon.”* <br>\n\nWhile many of these conclusions have stood the test of time, there are many lingering questions about the nature of time-series forecasting that are left unanswered. While the value of forecast combination, out-of-sample validation, selection of accuracy measures, forecast horizon all became an important consideration for forecasters, the first claim about simple versus complex models needs further investigation in the explosion of machine learning and artificial intelligence in recent years.  Although M3-competition was stated to be the last of its kind, it was not to be, and for good reason. The explosion of machine learning and artificial intelligence calls for a relook of the conclusions of previous M-competitions. In 2018, M4-competition was conducted with a bigger number of time series and inclusion of machine learning methods in the forecaster’s repertoire.\n \n","metadata":{}},{"cell_type":"markdown","source":"### *The Changing Landscape of Forecasting*\n\n\nSince the establishment of the four tenets of time-series forecasting after the early M-competitions, the data landscape has changed exponentially.  First, the nature of the problems encountered in the modern business environment has no resemblance to the time series tested in early M-competitions. Until M-4, the M-competition datasets were skewed towards longer time intervals, such as years, months. However, with the advent of the internet and the amount of ‘big data’ it generates, the time frequency of practical time-series problems nowadays is in the order of minutes, hours, days, and weeks. In addition, the forecaster is likely going to work with messy data these days, often containing a lot of missing values. The time-series until M4- competition did not permit any missing values, and all values were positive to allow for traditional measures of accuracy. Given the new reality, the previous findings from M-competitions require stringiest scrutiny on forecasting problems that represent the current reality.    \n\nSecond, the development of new machine learning (ML) models and their promise in time series forecasting is hard to ignore. While the pure ML models did not perform very well in the M4-competition, the combination of ML with traditional models showed promise. This is sufficient to suggest that probably the initial conclusion about the simple model vs complex model needs a thorough review.  The reason for perceived failure of ML models until this point may have stemmed from the simple fact that ‘forecasts require more information than the time-series’ (Fry & Brundage, 2020).  The comparison of statistical models against ML models will not be fair if we deprive the ML model from useful information on exogenous variables, and hierarchical relationship between the time-series. \n\nThird, there is also another development that has changed the forecasting community for the better. It is the democratization of forecasting. The emergence of data science as an attractive profession has made forecasting an essential skill in the labor market. This has also precipitated in many people from other disciplines learning forecasting, often bringing their domain expertise into forecasting. The type of analysis. Now, the Kaggle competitions see a vast majority of its participants from developing countries. The same cannot be said of early M-competitions, which had very few participants with access to expensive computing resources and graduate level training in statistical methods. People scramble to learn every new skill to stay relevant in the job market.  The modern data scientist is pragmatic, and focuses on getting the job done, rather than worrying about why. \n\nFinally, an important catalyst of these transformations is the emergence of a vibrant data science community with Kaggle at its vanguard. Founded in 2010, Kaggle provides a broader platform for data scientists, analysts, and economists to showcase their skills and methodologies in data analysis. Over the years, Kaggle has attracted participants from various disciplines and has had a significant impact. Since its inception, Kaggle has continuously expanded its scope by incorporating new analytical methods and techniques coherent with the general latest advancements in time series analysis. Additionally, it provides real-life datasets with access to external information, especially in recent competitions. Nevertheless, Kaggle competitions do represent the broader trends in methodologies and techniques in time series forecasting. Kaggle platform also aims to leverage the collective wisdom by providing a live leaderboard that allows multiple submissions over the duration of the competition leading to improved results (Athanasopoulos & Hyndman, 2011). These competitions have undoubtedly played an important role in the advancements in new methodologies in forecasting analysis. What differentiates the Kaggle platform from the rest of the academic competitions are its discussion forums where it allows the wisdom of the masses to collaborate and discuss potential strategies and also its focus on solving a problem rather than learning a method (Bojer & Meldgaard, 2021). This further demonstrates Kaggle's acceptance of different preferences and skill sets in the competition while ensuring that the platform remains an equitable space for the diverse forecasting community (Bojer & Meldgaard, 2021).\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"### *A Relook into the Nature of Forecasting*\n\n\nGiven the new reality of time-series forecasting in the era of big data, the conclusions of early M-competitions require further scrutiny of real-world forecasting problems with messier datasets. The recent Kaggle competitions provide an excellent opportunity to achieve this task. The academic forecasting community could not ignore the good things happening in Kaggle, and the M-5 competition was hosted in Kaggle in early 2020, with an unprecedented amount of response from Kagglers. We provide a detailed investigation into the findings of the M5-competition in the later parts of this essay, along with several other recent Kaggle competitions. \n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Scope of the Study\n\nIn view of the background, we analyze some recent competitions in Kaggle, and some of the tenets that emerged from the early M-competitions. Our review also includes M competitions which marks the historical evolution of forecasting competitions, with the recent M competition, M5, hosted by Kaggle. Apart from reviewing these fundamental questions about the nature of forecasting, we also touch upon some emerging issues, previously not given enough attention in forecasting. <br> \n\n*Data* \n\nWe use the *\"all competitions\"* dataset from Kaggle, which provides information about Kaggle competitions launched between August 2010 and February 2023. We identify the competitions involving time series data by inspecting the description of each competition. Among these competitions, the highest number of time series competitions occurred in 2020, totaling seven competitions. At the same time, it is more important to observe that out of the 108 Kaggle competitions from 2020 to 2023, only 14 were time series. From the dataset, we selected the time series competitions launched from 2021 onwards for our analysis. The overview of these selected competitions is presented in Table 1, including a review of the data used and successful solutions. Out of 310 competitions in the Kaggle competitions dataset, we found that 37 used a time-series dataset, accounting for approximately 12% of competitions. Figure 1 presents a graph of the number of Kaggle time-series competitions over the years.\n\nDespite the absence of significant variations in the number of competitions, a notable difference in the number of participants is observed from 2020 to 2022. The highest number of participants was recorded in 2020, totaling 14,942. In the subsequent years, 2021 saw 7,419 participants, while 2022 had 9,348 participants, specifically in time series competitions. In addition to the competitions launched from 2021 onwards, we include the M5 competition launched in 2020 in our study due to its importance in forecasting and its extended competition period. Bojer & Meldgaard (2021) provides an excellent review of previous time-series competitions in Kaggle outside our study period for an interested reader.\n\n**Figure 1: Number of Time-series Competitions between January, 2010 and February, 2023**\n\n![number_of_competition.png](attachment:2cab8278-54d6-4482-9c60-b4b36a15a97f.png)","metadata":{},"attachments":{"2cab8278-54d6-4482-9c60-b4b36a15a97f.png":{"image/png":"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ymlM9uaNw4PD1euv/76T+ac33TQr/SPyTlviIgNExMT99VqtV8ZHR0dmc/YymCp1rdDSD09PX/VauScX3PZZZdNLmRAS8lSGZNajzk0NHTBxMTEn0XEL0XEsRHxVznnqFarsXXr1oMPaUREdXR0dMt8x1YGSyWXms3mt9o+m1UvvPDCY6+99tp7D9V/zZo1vXH/gtGIiGg0GhZkHaEi1LcixEgxXifzicVQhPrW7VySm91XhjHJXDeLhTNkAV1Vq9UetmfPnm/nnN/Qtvvy0dHRzx/UdVVro9ls/nQOD3FnayOldMwRhtmpIsS4pC2xfGIBlDGH5vCcmYMy5FKj0Ugx/UF5BrdMTk667NyDNMf3Z2FyaWhoqK/RaHxxunlvs9msmhSbX0ttTEopPbat+fqI6J/lkGfmnG+++OKLnziPYZXCUsqllFL7JO9Pt27dOpJzftMshx2Tc/7barX6u/MZ21K3VOvbodRqtTUR8bTp5o2bNm3atIDhLClLaUxqGRkZ+dnevXvfHBGdfNnz1/39/V+cvRuzWUq5dN55590QEXe12v39/b93mO5p5cqVH42I9ksWRk9Pj++BjkAR6lsRYqQYr5P5xGIoQn3rdi7Jze4ry5hkrpvFwh/iQFcMDw9XarXaxpzztulfr7f8WX9//y/FQad8TCmtaNveM4eHarRt9xyyVxcUIcalainmE0dXGXNors+ZzpQxlw7hOT09Pd+pVqv/faEDKaIjeX8WKZcmJiY+GRGPmm5evHnz5rsO158jt4THpHPaGznnz6WUntzf339MvV6v9Pf3L4+IJ+acP9fWra9SqXx97dq1xx+F+JacpZhLOeeHtDXfHBGXRkSklP6o2Ww+evfu3cvq9XolpXR8SunCiLilrf87BwcH18xnfEvRUq9vh5Byzpftb6T0tvB39oO2FMekllqtdnFfX98dEXH2rJ0jXjkxMfGDSy655Kx5DmvJWoq5NP1Dh9e02jnnN1Wr1SsvvvjiJ2zcuHFZRMTGjRuXVavVc6vV6lcj4tcPjnFqaspZ/OagCPWtCDFSjNfJfGIxFKG+dTuX5Gb3GZMOYK6bo8YlC4EHrVqtnrN169bPR8Tj23bvjYgX1+v1GX8pmnOeam2nlOayOHR523ETc411LooQ41K0VPOJo6eMOXQkz5nZlS2Xrr766lsiIkVEWrNmzfLjjjvujJTSiyPityKi9Yuhj9Zqta2jo6NfWogYi+hI359FyaXBwcFq7DujUeScP7Jp06Yvz/djltVSHpNyzk+d3mzknC/atGnTAWcHGRkZ2RsRN0bEy6rV6uci4srpm1b29vZ+NCLeON8xLiVLNZcqlUp/26UJl0VENJvNX9i8efM17f1GR0d3R8TfV6vVsyPi32L6kpkppT+JiEfPZ4xLyVKvb4eyfv36ZzabzZ+bbv5odHR0bKFiWSqW6pgUEVGtVp+fc25/Drc0m8039vT0fH10dPSeNWvW9JxwwgmnNJvNV0fE70z3ObPRaPzfiy666MxrrrlmLmchKL2lnEv1en20Wq1+NiJePr1rfaVSWb99+/aoVquxffv2gw95V0S8OyJWRkQsX778nvmOcakoQn0rQowU43Uyn1gMRahv3c4ludl9ZRyTzHWzWFiQBRyxoaGhFRMTE78XEW9p359SumLPnj2vn2XiqP22FYfs9UAPbW3knO88XMcuKEKMS0YJ8ol5VsYcepDPmUMoYy4dJI+NjU1ExA8i4kMbNmz41NTU1Pci4uSIiOmzQjw2/BLtsLrw/lz0ubRu3bqHp5T+brp5w4oVK949n49XVmUYk+r1+n+ZQ9+rqtXqhyPiHdO7Ng4NDf36yMjIz+YnuqWjBLk0dVD7Dw5ejNWuXq/fV6vVfiXn/A/Tux51ySWXnHXllVf+57xFuASUob4dTqPR+GhKqdV8Z/h76Igt9TFp7dq1yyNitNVOKX314Q9/+Asuu+yy/WcqGhsbm4qIWyPiA4ODg3+bUrox9i0oPbavr+9jEfGq+YxxqVjqudR6mNWrV7/q+uuv/17O+f2H65hS+q2c8x9ExP7L8d511133znuEBVeE+laEGCnG62Q+sRiKUN+6nUtys/uMSftCMNfNQrIgCzgitVrtSRMTE2MR0X5ZiJtzzq+o1+vfmO34lNItbb9ePq2Tx1yzZk1vtF1qNed8d+cRz10RYlwqypBPzK8y5tCDfc7MrIy5NJsrrrjizmq1+oqIaH2p/XO1Wu2U0dHRbQsZ12LWjfdnAXIp9fT0XBP7vjScbDQaF42MjDRmO4i5MSbNLKX0v3LOrQVZsWfPnsdExA0LGNKiV4Zcyjkf8GVzSunjsx1z7rnnfnnr1q0TEdEfEdFoNJ4QEf85LwEuASWpb4d08cUXPyGldP50s7l79+4rFiKOpaAMY9KyZcsuyjkf22pPTk6+uH0x1sE2bdr0g8HBwdenlD4zveuV1Wr1jfV6/b75jLPoypBLLdOXLvztSy+99I8nJyd/OSIGI+K82HcZoH9JKY02Go0/37x5800XX3zxoyqV/SHeO/0lJIdQhPpWhBgpxutkPrEYilDfup1LcrP7jEkzM9fN0WZBFjBn1Wr1xTnnz7ftakTEW/v7+/+40y/ics63tG0/tpNjVqxYcUx7e9myZQ84H3c3FSHGpaAs+cT8KWMOdeM580BlzKVO7d69+ysrV65s3/XIiPAhdQbden8u9lxas2ZNT0Q8qfVQPT09P6pWqzP2nZiYqBzU3l2tVpvTzS/V6/WL5ivOIjMmHdro6Ogd1Wp1b0yfYj7n/MiwIOuQSpRLP2pvTE5O3jrbAcPDw81qtfrFiLg4IiKl5JKFh1CW+nY4lUrlbW3NP7bA4ciUaExa27b9pS1btozPdsCKFSsun5iYaC3Iikql8uiI+O58BLcUlCiXDnD55Zdvj4gPTf+bUaVSeVxb87p5D6rAilDfihAjxXidzCcWQxHqW7dzSW52nzHp8Mx1czRZkAXMSa1We95BBfKbvb29tSuuuOKOudxPzvk7baf5f1onx/T29p7R1tx75ZVX7pzLY85VEWIsujLlE/OjjDnUrefMgcqYS3MxNjY2Ua1W959BpP3X/tyvm+/PAubSsjn07Zn+N9fjSsOYNKscEXdGxCkRESklp5U/hJLl0m3tjWc84xmTW7Zs6eS4/ZPDOWdj0gxKXt8iImLjxo3Ltm/fvrHVTil98mjHsBSUaUzKObcviPl6J8eMjIzcU61Wd0fEyun7eHhYkDWjMuXSEfqF1kZK6eqFDGQxK0J9K0KMFON1Mp9YDEWob93OJbnZfcak2Znr5miqzN4FYJ+1a9cen3Pe1LbrL/v7+591JAWyp6enfULptFqttvKQnaflnJ/S2k4pbY55vp5vEWIssrLlE91Xxhzq5nPmfmXKpaGhoRXVanVv69+ll156yhwOby2gcZmCGXT7/bnYc4n5U6Yx6UgNDw9XYnox1rRZz4RURmXLpampqf9sb2/durW/k+Nyzme1bculg6hv+2zbtu05cf/fQnePjo5aJDNHZRuTIqI5e5cZ7Wlt5Jz3dimWJaWEuTRXKSJe1Wo0Go0vLmAsi1YR6lsRYqQYr5P5xGIoQn3rdi7Jze4r25hkrpsisCAL6Fhvb+9vRMQxERE556/39/f/ypGeNvKqq676cUTc1Wo3m80LZjumUqm8sa3/Z4/kceeiCDEWWdnyie4rYw518zlzvzLl0sjIyETs+xC8LCKW7d279+xOjtuwYcNJ0XYmo2XLlv3nvARYYN1+fy72XBobG5uq1+upk3/9/f0HnJl5z549x7Xd/vz5jLOIyjQmRURUq9W/bU2c1Wq113ZyzLe+9a32s47E8uXLb5qf6IqtbLk0fTmw9kshnDPbMUNDQz0ppee02jnnG+cjtiIrW307TAxvbW2nlD4dvpCes7KNSdF2Zqv2ceZwNm7cuCwiTm7bdXO3g1oKypZL69evP7/ti8Yvzda/Wq0+NSIeMt3c/fSnP/1f5zfCYipCfStCjBTjdTKfWAxFqG/dziW52X1lG5PMdVMEFmQBHRkaGuqJiP/WajcajZc8yD+Mcs75Y61GSukj0790n9G6desekXPeX+gbjca1D+KxO1WEGAuppPlEF5Uxh+bhOROlzKUcEX/f1n55Jwc1Go32S/T8+IorrhjvdmBFNk/vz8WeSx0755xzDvjS+oQTTjjSM0YseSUckyKl9JWYnjjLOf9a7Durw2H19PR8oK153cjIiEunHKSMuTQd15+0Nd8/W/+JiYkLI6JvunnX05/+9H+fl8AKSn3bZ82aNb055/Wtds5589GOoejKOCblnK9r276gVqudOtsxP/nJT57d1tzd399/2yE7l1QZc2lycvK2mP5bKSLWDA0N9c1yyEfatj80PDzsb++DFKG+FSFGivE6mU8shiLUt27HKDe7r6RjkrluFj0LsoCO7Nmz55yYvpZuRFy7ZcuWHz/Y++zr67usrXnO9ddf/5aZ+g0NDa3o6empt+36wy1btux6sI/fiSLEWERlzSe6p4w5NB/PmXLmUkR8srWRUnptrVZ70uE6r1+//vyc8wfbdr0vnBXiAPP1/ixALtFlZRyTGo3G1W3NJw0ODs4YX0u1Wn1ZznlDq51Seve8BVdgZcylaZ9o2/6FWq32y4fqODQ0dEJE/HmrnXN+ty+tD6S+7XPCCSc8NtrmUHt6eq4/2jEUXRnHpJTS1dF22cJmszkyfQasGQ0NDZ2Qc/5c2/Ef9aXkA5Uxl84777xbI2L/40xMTLzzUH2r1eo7I+IF081mSunj8xxeIRWhvhUhRorxOplPLIYi1Lduxyg3u6/EY5K5bha13tm7AETknJ/Z1rywWq3uPYK7eX69Xv9aq3H55Zdvr1arH46Id0w/xscHBwdPmpyc/Pg111zz040bNy7btm3b0yYmJj4TEY+ZPuy+/v7+dx3xE5mjIsRYRGXNJ7qnjDk0H8+ZcubS6tWrr9m6deu/R8TjpuP7l2q1+s6c8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Table 1: Selected kaggle competitions and their associated forecasting tasks\n| |                                             |            |                                                                                  |                  |                                                               |                                |\n|------------------------------------------------------------------------------|---------------------------------------------|------------|----------------------------------------------------------------------------------|------------------|---------------------------------------------------------------|--------------------------------|\n| Year                                                                         | Competition                                 | Time Unit  | Forecast Unit                                                                    | Forecast horizon | Solutions                                                     | Link                           |\n| 2021                                                                         | MLB Player Digital Engagement Forecasting   | Daily     | fans engage with MLB players’ digital content:4 different measures of engagement | One month        | neural network consisting of a GRU, LSTM, CNNs, LGBM, Xgboost | https://www.kaggle.com/c/27783 |\n| 2021                                                                         | Optiver Realized Volatility Prediction      | 10 minutes | Price                                                                            | 3 months         | LightGBM, Tabnet, Weighted XGBoost                            | https://www.kaggle.com/c/27233 |\n| 2021                                                                         | G2Net Gravitational Wave Detection          | 2 sec      | Presence of GW signal                                                            |                  | ResNet34 model                                                | https://www.kaggle.com/c/23249 |\n| 2021                                                                         | G-Research Crypto Forecasting               | Daily      | Future returns of crypto                                                         | from 2018        | LGBM and Keras NN models                                      | https://www.kaggle.com/c/30894 |\n| 2022                                                                         | Ubiquant Market Prediction                  | Daily      | Investment Return Rate                                                           |                  | XGB and LGB                                                   | https://www.kaggle.com/c/32053 |\n| 2022                                                                         | March Machine Learning Mania 2022 - Women's | Yearly     | Outcome - win/lose                                                               |                  | AutoLGBM                                                      | https://www.kaggle.com/c/34542 |\n| 2022                                                                         | March Machine Learning Mania 2022 - Men’s   | Yearly     | Outcome - win/lose                                                               |                  | AutoLGBM                                                      | https://www.kaggle.com/c/34538 |\n| 2022                                                                         | American Express - Default Prediction       | Monthly    | Credit default                                                                   |                  |  LGBM or XGB                                                  | https://www.kaggle.com/c/35332 |\n| 2023                                                                         | March Machine Learning Mania 2023           | Yearly     | Outcome - win/lose                                                               |                  |  XGBoost                                                      | https://www.kaggle.com/c/46801 |\n","metadata":{}},{"cell_type":"markdown","source":"# Review of Recent Kaggle Competitions\nTo conduct the review, we collected all the time series competitions in Kaggle forum from 2021 to 2022. Solutions of the winning entries are reviewed here based on the information collected from the forum. Table 1 shows all the time series competitions that have been conducted in the Kaggle platform since 2021. The review will include the techniques and models for the best solutions used by the winning teams. \nAs mentioned in the above sections, we can observe the valuable contributions of collective wisdom in effectively addressing real-life and real-data problems across the presented competitions. Furthermore, in most cases, the solution requirements necessitated the utilization of new techniques in machine learning, underscoring the substantial progress made from relying on simple methods to seeking sophisticated and optimized approaches for real-time solutions to complex big data problems. Kaggle’s past competitions along with the efficiency prize aimed at promoting green AI, not only establish Kaggle as a democratic platform for developing advanced and optimized solutions, but also contribute to the collective effort of reducing the carbon footprint. \nIn the following section, we present the findings based on our review of the 8 competitions and the successful solution methods used.\n\n\n**M5-Competition:** <br>\nM5 competition is the most recent among the M competitions in forecasting. The M5 competition took place from March to June 2020 and focused on Walmart, a prominent player in the retail industry, involving daily hierarchical series of product sales. Hosted by Kaggle, the competition saw a notable increase in participation from Kagglers. This sharply contrasts previous editions of the M competitions, which had fewer participants, primarily researchers in forecasting. \n\nThe objective of the competition was to generate the most accurate point forecasts for 42,840 time series, which represented the hierarchical unit sales of retail stores across different locations (Makridakis, Spiliotis, et al., 2022). The accuracy competition employed the Weighted Root Mean Squared Scaled Error (RMSSE) measure, while the uncertainty competition utilized the Weighted Scaled Pinball Loss (WSPL) measure to assess the deviation of probabilistic forecasts from the realized values (Makridakis, Spiliotis, et al., 2022; Ord, 2022). Furthermore, M5 data included exogenous variables, such as information about prices and special events. Accuracy and Uncertainty competitions are different in many aspects such as (i) submission template, (ii) performance measure, (iii) prizes, and (iv) benchmarks. But they are similar regarding top performing models. Both Accuracy and Uncertainty top models employed a particular tree-based forecasting method, LightGBM, advanced, deep NNs, or combinations of those. These methods achieved significant improvements in terms of accuracy and uncertainty over the statistical benchmarks (Makridakis, Petropoulos, et al., 2022; Ord, 2022). \n\n\n**MLB Player Digital Engagement Forecasting:** <br>\nThis time-series competition occurred in 2021 with 852 teams participating. Participants in this competition aim to identify the factors that enhance supporter/fan engagement and foster stronger relationships between players and fans. Since the 1990s, MLB has been at the forefront of the sports world, showcasing the possibilities that arise when combining data with human performance. In the age of big data, MLB has partnered with Google Cloud to revolutionize the fan experience by leveraging data through the new AI platform called Vertex AI. \n\nThe solutions presented by the winning teams aimed to predict daily digital fan engagement with MLB players' digital content, demonstrating a profound understanding of America's favorite pastime. The participants had access to player performance data, social media data, and team factors like market size. The analysis of the first three winners reveals the utilization of machine learning techniques, including random forest, feature selection, feature engineering, and lag feature, coupled with mean squared error. This method is the first of its kind to understand the digital engagement at the player level in a day-to-day fashion.\n\n\n**Optiver Realized Volatility Prediction:** <br>\nFor trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product. Therefore, this competition aims to discover solutions by constructing models that predict short-term volatility for hundreds of stocks across various sectors. Participants in this competition are given access to millions of rows of finely detailed financial data to develop models for forecasting volatility over 10-minute periods. The data provided gives valuable insight into the volatility and financial market structure of data science problems that Optiver has faced for decades.\n\nA compilation review provided by Kaggle, based on successful solutions, included methods such as Fast NN, LightGBM, Tabnet, Weighted XGBoost, and other machine learning techniques. Interestingly, an increasing number of machine learning methods appear to achieve superior performance and yield the expected outcomes. \n\n**G2Net Gravitational Wave Detection:** <br>\nG2Net is a network of Gravitational Wave, Geophysics and Machine Learning. G2Net aimed to create a broad network of scientists with the support of a funding agency for research and innovation networks, COST (European Cooperation in Science and Technology). Hence, this competition aims to detect GW signals from the mergers of binary black holes by building models to analyze simulated GW time-series data from a network of Earth-based detectors. Participants were provided with a training set of time series data containing simulated gravitational wave measurements from a network of 3 gravitational wave interferometers (LIGO Hanford, LIGO Livingston, and Virgo). Each time series contains either detector noise or detector noise plus a simulated gravitational wave signal. The task is to identify when a signal is present in the data (target=1).\n\nThe winning teams used deep learning methods, ResNet34 model. Many other teams used 2D and 1D models and various augmentations of 2D images. The basic models were followed by optimization models as well to further improve the results.\n\n\n**G-Research Crypto Forecasting:** <br>\nThis competition specifically asks for machine learning (ML) expertise to forecast cryptocurrency returns using historic trades for several crypto assets, such as Bitcoin and Ethereum. G-Research is Europe’s leading quantitative finance research firm. Cambridge Spark, which specializes in data science and AI education for workforces, partnered with G-Research for this competition.\n\nThe participants needed to use machine learning expertise to forecast short term returns in 14 popular cryptocurrencies. The dataset included millions of rows of high-frequency market data dating back to 2018. Solutions for the prediction issues used methods such as lagged and timestamp averages, ensembles of LGBM and Keras NN models, Target Engineering and prediction switching.\n\n**Ubiquant Market Prediction:** <br>\nEstablished in 2012, Ubiquant Investment (Beijing) Co., Ltd is a leading domestic quantitative hedge fund committed to creating long-term stable returns for investors. The aim of this competition is to use new techniques and approaches in data science to improve quantitative researchers' ability to forecast an investment's return. \n\nThe dataset included features derived from real historic data from thousands of investments and the challenge was to predict the value of an obfuscated metric relevant for making trading decisions. In this analysis, the competitors were discussing more about managing the large dataset for the analysis and hence discussed different methods like the DASK approach to process large datasets. The successful solution used XGB and LGB in ensembles for trained data and random pick of features for testing was also used to have different features for investment. This was a better risk management approach. The solution suggested iterating per row and looking for known trained assets for prediction otherwise take the mean of already predicted time-id, in bigger market moves, the majority generally follows, it was better than a zero prediction.\n\n\n**March Machine Learning Mania 2022 - Women's and Men’s:** <br>\nWe are reviewing both the competitions- Women and Men- together here. Both were conducted separately in February 2022. Kagglers joined millions of fans to predict the outcomes of women’s and men’s college basketball tournaments for 2022 using NCAA historical games data. The Kagglers were allowed to use other publicly available data as well.\n\nThere were two stages in the competition. In stage one participants will build and test their models against previous tournaments. In the second stage, participants will predict the outcome of the 2022 tournament. Logistic prediction methods were used in one of the winning papers. The AutoLGBM method was the most used by the winners.\n\n\n**American Express - Default Prediction:**  <br>\nAmerican Express is a globally integrated payments company.  In this competition, an industrial scale data set was used to apply machine learning techniques to predict credit default. The aim was to help create a better customer experience for cardholders by making it easier to be approved for a credit card. Training, validation, and testing datasets include time-series behavioral data and anonymized customer profile information. LGBM or XGB is found to be the best approach to the solution by the winning teams. \n\n**March Machine Learning Mania 2023:**  <br>\nThis is the 9th March Machine Learning Mania competition to predict the outcome of this year’s college basketball competition. As mentioned in the earlier version of this competition, the dataset used is the historical NCAA games to forecast the outcomes of the Division 1 Men's and Women's basketball tournaments.\n\nThis year’s competition is different from the previous versions in terms of the evaluation metric. The metric has changed from log loss to Brier scores. Also, they are combining both Men’s and Women’s tournaments into one single competition this time. Another change is the prediction format. Instead of waiting for the team selection for the tournament, you can right away do the prediction. AutoML, XGBoost, ElasticNet, LogisticRegression have been tested to provide the best prediction solution.\n","metadata":{}},{"cell_type":"markdown","source":"# Learnings and the Path Ahead\nOur review of recent Kaggle competitions on time-series forecasting and prediction problems confirmed some of our initial suspicions. While some fundamental conclusions about the nature of forecasting remain unchallenged, there is enough evidence to suggest that winds of change are blowing. The M5-competition and other recent time-series Kaggle competitions had datasets that closely resembled real-world problems with additional exogenous information. Confirming the suggestions of (Fry & Brundage, 2020), the ML models dominated the successful solutions in these competitions. This suggests that the previous poor performance of ML models in M-competitions was because the models were deprived of the type of data they could thrive on. While it may be too early, we can safely conclude that the complex models will do well if allowed to work on complex real-world problems with enough exogenous information to train on. However, we do not suggest that simple models are undesirable. Simple statistical models will continue to be useful as benchmarks, and they remain easily understood. Moreover, in applications where ML models are less acceptable due to concerns about explainability, statistical models will continue to be used.   <br> \n\nOur study essentially reinforced the other conclusions of early M-competitions. There is a growing understanding that more than merely accuracy is needed to compare forecasting models, and other criteria such as uncertainty, cost-effectiveness, etc. matter. Most competitions nowadays measure multiple evaluation criteria. The recently concluded M5-competition had two tracks of accuracy and uncertainty, and the winning solutions came from different methods. In addition, intelligent combinations of models using the ensemble approach have become popular in machine learning. The recent competitions were also varied in terms of forecast horizons. \n\nKaggle's main contribution lies in diversifying forecasting by bringing together a wide variety of problems, methods, and people, creating a synthesis of knowledge in the field. The forecasting problems hosted at Kaggle, especially the recent ones, represent a wide variety of fields, including finance, economics, sports, retail, and geophysics. The competitions held at Kaggle also comprised various complex datasets and higher entropy than comparable M-competitions. With more software and computing resources becoming easily accessible, anyone could enter a forecasting competition and compete for prizes. The diversity of people who participate in forecasting competitions is changing. It is worth noting the contribution of the KaggleX mentorship program in popularizing data science among historically underrepresented populations. When achieving efficient and effective solutions, the Kaggle platform excels in providing optimized solutions for real-world data problems. This is evident in past competitions, where companies have stepped forward to find solutions using real-world data. On certain occasions, these competitions have resulted in the creation of job opportunities for competitors. These facts prove that Kaggle is efficient in democratizing the platform, reducing its carbon footprint, and offering real-life solutions to help people. These facts demonstrate that Kaggle is efficient in democratizing the platform and reducing the carbon footprint, and offering real-life solutions that help people. While we acknowledge the democratic nature of the platform, providing opportunities for members to showcase their skills and contribute solutions to real data problems, it is essential to address the limitations inherent in this democratic approach. Bojer & Meldgaard (2021) highlight the limitation concerning what the researchers can infer from the competition. The unavailability of test data makes it challenging to discern differences in the performance of different solutions or to evaluate performance using alternative error measures. In addition, a democratic platform should always involve a reciprocal exchange. While Kaggle promotes the sharing of solutions, contestants are not obliged to publicly disclose their solutions or code, which hinders the learning process from competitions (Bojer & Meldgaard, 2021).\n\nOur analysis of the Kaggle platform competitions reviews previous findings from M-competitions regarding time-series forecasting and initiates discussions on new issues. As the world is increasingly moving towards optimized and highly technical AI models in the fields of machine learning, data science, and problem-solving with big data, delivering impressive accuracy in solutions and providing democratic platforms, it becomes crucial to consider the environmental cost while striving to enhance the quality of life in all possible ways. In the Kaggle essay titled 'Towards Green AI,' the author highlights the need for larger hardware for improved computations, resulting in increased energy usage and a corresponding rise in CO2 emission. According to Strubell et al. (2019), there are differences in the availability of resources between industry and academic research. Industry research has the advantage of utilizing in-house resources with high computing hardware. In contrast, academic research often relies on centralized resources that provide equitable access to all researchers, thereby limiting the carbon footprint (Strubell et al., 2019). In the Kaggle essay 'Towards Green AI,' the author notes that the Efficiency prize motivated the Kaggle participants to explore various techniques to reduce carbon emissions during the inference stage. The author also confirms that though the Kaggle community uses academic techniques, the design of models, however, contributes to the carbon emissions in the training stage. Hence a more holistic approach is needed to reduce carbon emissions during the competition and at every research and optimization stage.\n\nThe increased success of ML models in time-series forecasting also raises ethical concerns about the applicability of such models in sensitive issues such as policymaking, credit access, etc. While we observe that the availability of exogenous information may help ML models become more accurate, we must be careful not to unintentionally produce biases against specific sections of society. A seemingly innocuous exogenous variable might be a proxy for race, gender, and other demographic information, and the models trained using such information may create bias. Another related concern while applying ML models in similar applications is the explainability of the ML models (or lack thereof) and how the features used in the training sample can explain the outcome. We expect a significant amount of research attention directed toward developing explainable AI models. \n\nWe acknowledge that our study is limited in its scope. We attempted to review eight forecasting competitions with different focus areas and numerous participants. Given these competitions' scale, complexity, and diversity, we could review only some of the winning solutions. The M5-competition had one special issue dedicated to the International Journal of Forecasting. Given, neither is our study comprehensive nor are our findings conclusive. However, we need more and more competitions representing real-world forecasting problems until we can build a diverse repertoire of forecasting methods in the toolbox of forecasters suited for different situations. In this aspect, we agree with the idea of 'horses for courses' while choosing forecasting models.   \n \n\n","metadata":{}},{"cell_type":"markdown","source":"<h1 id=\"sec:reference\">References</h1>\n\n**Literature**\n\nAlsharef, A., Aggarwal, K., Sonia, Kumar, M., & Mishra, A. (2022). Review of ML and AutoML Solutions to Forecast Time-Series Data. Archives of Computational Methods in Engineering, 29(7), 5297–5311. https://doi.org/10.1007/s11831-022-09765-0  <br> \nAthanasopoulos, G., & Hyndman, R. J. (2011). The value of feedback in forecasting competitions. International Journal of Forecasting, 27(3), 845–849. https://doi.org/10.1016/j.ijforecast.2011.03.002  <br> \nBojer, C. S., & Meldgaard, J. P. (2021). Kaggle forecasting competitions: An overlooked learning opportunity. International Journal of Forecasting, 37(2), 587–603. https://doi.org/10.1016/j.ijforecast.2020.07.007 <br> \nFry, C., & Brundage, M. (2020). The M4 forecasting competition – A practitioner’s view. 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Time, Power, and the Maya. Latin American Antiquity, 19(3), 275–298. https://doi.org/10.1017/S1045663500007951  <br> \nShen, Z., Zhang, Y., Lu, J., Xu, J., & Xiao, G. (2020). A novel time series forecasting model with deep learning. Neurocomputing, 396, 302–313. https://doi.org/10.1016/j.neucom.2018.12.084  <br> \nStrubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP (arXiv:1906.02243). arXiv. https://doi.org/10.48550/arXiv.1906.02243  <br> \n\n\n\n**Kaggle Writeup**\n\nLeonie (2023). Kaggle Efficiency Writeups - 2023 Kaggle AI Report\n","metadata":{}}]}