Kaggle forecasting competitions: An overlooked learning opportunity

Competitions play an invaluable role in the field of forecasting, as exemplified through the recent M4 competition. The competition received attention from both academics and practitioners and sparked discussions around the representativeness of the data for business forecasting. Several competitions featuring real-life business forecasting tasks on the Kaggle platform has, however, been largely ignored by the academic community. We believe the learnings from these competitions have much to offer to the forecasting community and provide a review of the results from six Kaggle competitions. We find that most of the Kaggle datasets are characterized by higher intermittence and entropy than the M-competitions and that global ensemble models tend to outperform local single models. Furthermore, we find the strong performance of gradient boosted decision trees, increasing success of neural networks for forecasting, and a variety of techniques for adapting machine learning models to the forecasting task

Position prediction of marine seismic streamer cables using various kalman filter methods

Towed seismic streamer cables are extensively employed for offshore marine petroleum exploration. With the increasing need for accurate streamer steering due to rising number and length of streamers and decreasing intrastreamer separation, as well as new types of survey configurations, accurate modeling, positioning, and path prediction of the streamers are imperative. In the present study, a variety of models and methods have been implemented and utilized for data assimilation of full-scale seismic streamer position data for a marine seismic streamer, followed by path prediction ahead of time. The methods implemented are described, including various models used with the Kalman filter, extended Kalman filter, and ensemble Kalman filter, with comparison and evaluation of prediction results. One particular method, the Path-In-the-Water ensemble Kalman filter (PIW-EnKF), appears to be the most robust method with good prediction results compared to the other methods, as well as having low computational cost. As a case study with full-scale data, the PIW-EnKF is further employed for estimation and prediction of a complete streamer spread

Platelet-, monocyte-derived & tissue factorcarrying circulating microparticles are related to acute myocardial infarction severity

Circulating microparticles (cMPs) are phospholipid-rich vesicles released from cells when activated or injured, and contribute to the formation of intracoronary thrombi. Tissue factor (TF, CD142) is the main trigger of fibrin formation and TF-carrying cMPs are considered one of the most procoagulant cMPs. Similar types of atherosclerotic lesions may lead to different types of AMI, although the mechanisms behind are unresolved. Therefore, we aimed to investigate the phenotype of cMPs found in plasma of ACS patients and its relation to AMI severity and thrombotic burden. In a cross-sectional study, two hundred patients aged 75±4 years were included in the study 2±8 weeks after suffering an AMI. Annexin V positive (AV)-cMPs derived from blood and vascular cells were measured by flow cytometry. Plasma procoagulant activity (TF-PCA) was measured through a chromogenic assay. STEMI patients (n = 75) showed higher levels of platelet-derived cMPs [CD61/AV, CD31/ AV, CD42b/AV and CD31/CD42b/AV, P = 0.048, 0.038, 0.009 and 0.006, respectively], compared to NSTEMI patients (n = 125). Patients who suffered a heart failure during AMI (n = 17) had increased levels of platelet (CD61)-And monocyte (CD14)-derived cMPs carrying TF (CD142) (P<0.0001 and 0.004, respectively). Additionally, NYHA class III (n = 23) patients showed higher levels of CD142/AV, CD14/AV and CD14/CD142/AV cMPs than those in class I/II (P = 0.001, 0.015 and 0.014, respectively). The levels of these cMPs positively correlated with TF-PCA (r≥0.166, P≤0.027, all). Platelets and monocytes remain activated in AMI patients treated as per guidelines and release cMPs that discriminate AMI severity. Therefore, TF-MPs, and platelet-And monocyte- MPs may reflect thrombotic burden in AMI patients