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yourquant_rick on Instagram: "Time series models, like ARIMA, are a classic framework for analyzing sequential data. They assume stationarity and lack of correlation, which works well for sales forecasting but struggles in finance. Markets are highly non-stationary — factors like inflation, rising costs, and constant shifts in sentiment affect price behavior. Correlation between assets is often temporary and only strong in high-sentiment markets like crypto or tech. I often combine uncorrelated

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𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍ENSEMBLE LEARNING – PART 10.3: BOOSTING Boosting is all about learning from mistakes. Instead of training one big model all at once, it builds a sequence of weak learners — usually simple models that are only slightly better than random guessing. But here’s the trick👇 Each new model pays extra attention to the examples the previous models got wrong. It “boosts” the importance of those mistakes so the next learner can correct them. Over tim

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