ARIMA is a well-known model for time series forecasting. In this post we will go over the building blocks: MA(q) models, AR(p) models and ARMA(p,q) models of such a powerful model.
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Attackers maintains fake accounts to use them for various reasons, for example, in a social media website, attackers could use them to post content to push a certain ideas. Another example in online retail where attackers use fake accounts to add fake reviews to improve the visibility of certain product.
In this post we will cover random walk based model proposed in Random Walk based Fake Account Detection in Online Social Networks to detect fake accounts.
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Buy-it-again is a popular recommendation model in online retail. In this post we will cover a simple model proposed in Buy it again: Modeling repeat purchase recommendations that achieves good results despite its simplicity.
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The Root Mean Square Error (RMSE) is a common loss function used in machine learning for regression tasks that measures the average distance between the predicted and true values. However, what implicit modeling assumptions are me making when we use such error to evaluate the model fit?
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Many of you already know that to in computer systems, to represent the negative numbers we use what is known as 2’s complement. In this article I will try to explain where it came from.
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The Luhn formula is a simple checksum formula used for validating identification numbers. It is designed to protect against accidental errors in data entry but not malicious attacks. For example, on a website a user may enter a credit card number or ISBN and sometimes these numbers may be mistyped. One can then use the Luhn formula to determine if the entered number is a valid one.
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