Скачать или смотреть FREE LSTM Stock Prediction Guided Project at DataSimple.education

What if your data model forgot the closing price from 10 days ago? (Most of them do).
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FREE Python Guided Project Level 1 with notebooks
https://www.datasimple.education/post...
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Most stock market 'prediction' models are fundamentally flawed because they forget the past.
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Think about it: A standard time-series model treats today's closing price the same, whether yesterday's price was up $50 or down $50, as if the price history didn't matter. . It's like trying to understand a novel by only looking at one word at a time. You lose the entire plot and all the context.
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I ran a simple ARIMA model on a well-known tech stock last month, and its error rate was abysmal. My failure wasn't in the math, but in the model's core architecture—it couldn't retain long-term dependencies.
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The most fascinating insight I learned during that project is this: The real magic isn't in adding more features (like RSI or MACD), but in giving your model a memory of the order of the data.
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The Reward: That's where Long Short-Term Memory (LSTM) networks come in. They have internal 'gates' (Input, Forget, Output) that literally allow the network to decide what information from the sequence to keep, what to throw away, and what to pass on.
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It means an LSTM model can look at the price action from a week ago, and three days ago, and weigh their collective impact on today's price—a temporal dependency standard models completely miss.
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It’s the closest we get to giving a machine true financial intuition.
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What's your 'go-to' strategy for dealing with the sequential nature of financial time-series data? Is market predictability truly a myth? I'd love to hear your take in the comments. .

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Want to see the Python code behind this high-memory model? The first part of the LSTM guide is here: https://www.datasimple.education/post... .


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