[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-10-08-extreme-events-en-US":3,"reports-algorithms":41},{"slug":4,"type":5,"extremeSubtype":6,"publishAt":7,"updateTime":8,"coverImage":9,"viewCount":10,"title":11,"metaTitle":12,"metaDesc":13,"summary":14,"content":15,"readMinutes":16,"related":17},"2026-10-08-extreme-events","daily",null,"2026-10-09 00:30:00","2026-10-09 00:30:36","\u002Fstatic\u002Fog\u002F2026-10-08-extreme-events.jpg",0,"Triple Number 555 Reappears, Unusual Cold and Hot Number Trends","Canada 28: Triple Number Returns, Cold and Hot Trends Spark Debate","Yesterday's Canada 28 draws saw the rare triple number 555 reappear, with cold numbers absent for 230 rounds and hot numbers clustering. AI algorithm performance and trends are under scrutiny.","Yesterday's Canada 28 results featured the rare triple number 555, a theoretical 1% probability event. Cold numbers were absent for 230 rounds, while hot numbers clustered between 10-15. AI algorithms achieved a 42.03% hit rate overall.","## Triple Number 555 Reappears, Probability Only 1%\nYesterday's Canada 28 draws caught my attention, especially with the rare reappearance of the triple number 555. Statistically, this event has only a 1% probability, making it an extraordinary occurrence. Players might wonder if this hints at more surprises in the upcoming draws.\n\n## Cold Numbers Absent, Hot Numbers Cluster\nOn the cold number front, four sums (0, 1, 25, 27) have now been missing for 230 consecutive rounds, which is quite unusual. Meanwhile, sums between 10 and 15 have been clustering, with sum 15 appearing 37 times, accounting for a daily share of 9.23%. How do seasoned players interpret such uneven cold and hot number distributions?\n\n## AI Performance: Cold and Hot Balance Algorithm Recovers\nThe AI algorithm's overall performance was moderate, with an average hit rate of 42.03%. However, there were highlights, such as the \"mean reversion\" approach achieving a prediction accuracy of 59.9%, leading the pack. Whether these algorithmic trends will persist remains to be seen in the coming days.\n\n## Span Concentration Shifts Higher\nSpan distribution data revealed that span 6 appeared most frequently, with 57 occurrences, dominating the chart. Spans 4 and 5 followed closely with 55 occurrences each. This concentration in higher spans might suggest an emerging trend.\n\nThese were the key takeaways I noted. What are your thoughts on these trends? Feel free to share your insights.",5,[18,27,34],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-10-13-extreme-triple-two","extreme","E1","2026-10-13 07:09:50","\u002Fstatic\u002Fog\u002F2026-10-13-extreme-triple-two.jpg","Canada 28 Triple Number 222 Appears! Probability Only 1%","Canada 28 draw #3493376 featured the rare triple number 222, with a probability of only 1%, marking the second occurrence within 24 hours.",2,{"slug":28,"type":5,"extremeSubtype":6,"publishAt":29,"coverImage":30,"viewCount":10,"title":31,"summary":32,"readMinutes":33},"2026-10-12-ai-performance","2026-10-13 00:30:00","\u002Fstatic\u002Fog\u002F2026-10-12-ai-performance.jpg","AI Algorithm Shines Yesterday with Extreme Events in Canada 28","Yesterday's Canada 28 results showcased an AI algorithm accuracy of 41.77%, alongside four extreme events like triple numbers and a rare sum of 0, sparking discussion among enthusiasts.",8,{"slug":35,"type":20,"extremeSubtype":21,"publishAt":36,"coverImage":37,"viewCount":10,"title":38,"summary":39,"readMinutes":40},"2026-10-12-extreme-triple-eight","2026-10-12 23:21:07","\u002Fstatic\u002Fog\u002F2026-10-12-extreme-triple-eight.jpg","Canada 28 Triple Number 888 Appears with Just 1% Probability","In draw #3493242, Canada 28 recorded the rare triple number 888, a result with only a 1% probability, appearing twice in one day.",3,[42,48,53,58,64,69,75,81,86,92,98,104,110,116,122,128,134,140,145,151,157,163,169,175,181,187,193,199,205,211],{"code":43,"nameZhCn":44,"nameZhTw":45,"nameEnUs":46,"sortOrder":47},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",1,{"code":49,"nameZhCn":50,"nameZhTw":51,"nameEnUs":52,"sortOrder":26},"deep_neural_network","深度神经网络","深度神經網路","Deep Neural Network",{"code":54,"nameZhCn":55,"nameZhTw":56,"nameEnUs":57,"sortOrder":40},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",{"code":59,"nameZhCn":60,"nameZhTw":61,"nameEnUs":62,"sortOrder":63},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":65,"nameZhCn":66,"nameZhTw":67,"nameEnUs":68,"sortOrder":16},"deep_learning","深度学习","深度學習","Deep Learning",{"code":70,"nameZhCn":71,"nameZhTw":72,"nameEnUs":73,"sortOrder":74},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",6,{"code":76,"nameZhCn":77,"nameZhTw":78,"nameEnUs":79,"sortOrder":80},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":82,"nameZhCn":83,"nameZhTw":84,"nameEnUs":85,"sortOrder":33},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",{"code":87,"nameZhCn":88,"nameZhTw":89,"nameEnUs":90,"sortOrder":91},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":93,"nameZhCn":94,"nameZhTw":95,"nameEnUs":96,"sortOrder":97},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":99,"nameZhCn":100,"nameZhTw":101,"nameEnUs":102,"sortOrder":103},"volatility","波动率","波動率","Volatility",11,{"code":105,"nameZhCn":106,"nameZhTw":107,"nameEnUs":108,"sortOrder":109},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":111,"nameZhCn":112,"nameZhTw":113,"nameEnUs":114,"sortOrder":115},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":117,"nameZhCn":118,"nameZhTw":119,"nameEnUs":120,"sortOrder":121},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":123,"nameZhCn":124,"nameZhTw":125,"nameEnUs":126,"sortOrder":127},"momentum","动量加速度","動量加速度","Momentum",15,{"code":129,"nameZhCn":130,"nameZhTw":131,"nameEnUs":132,"sortOrder":133},"quantile","分位数","分位數","Quantile",16,{"code":135,"nameZhCn":136,"nameZhTw":137,"nameEnUs":138,"sortOrder":139},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":141,"nameZhCn":142,"nameZhTw":142,"nameEnUs":143,"sortOrder":144},"local_entropy","局部熵","Local Entropy",18,{"code":146,"nameZhCn":147,"nameZhTw":148,"nameEnUs":149,"sortOrder":150},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":152,"nameZhCn":153,"nameZhTw":154,"nameEnUs":155,"sortOrder":156},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":158,"nameZhCn":159,"nameZhTw":160,"nameEnUs":161,"sortOrder":162},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":164,"nameZhCn":165,"nameZhTw":166,"nameEnUs":167,"sortOrder":168},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":170,"nameZhCn":171,"nameZhTw":172,"nameEnUs":173,"sortOrder":174},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":176,"nameZhCn":177,"nameZhTw":178,"nameEnUs":179,"sortOrder":180},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":182,"nameZhCn":183,"nameZhTw":184,"nameEnUs":185,"sortOrder":186},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":188,"nameZhCn":189,"nameZhTw":190,"nameEnUs":191,"sortOrder":192},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":194,"nameZhCn":195,"nameZhTw":196,"nameEnUs":197,"sortOrder":198},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":200,"nameZhCn":201,"nameZhTw":202,"nameEnUs":203,"sortOrder":204},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":206,"nameZhCn":207,"nameZhTw":208,"nameEnUs":209,"sortOrder":210},"number_combination","数字组合","數字組合","Number Combination",29,{"code":212,"nameZhCn":213,"nameZhTw":214,"nameEnUs":215,"sortOrder":216},"recent_chain","近链推演","近鏈推演","Recent Chain",30]