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on 13-Jan-2023 (Fri)

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#recurrent-neural-networks #rnn
n this specific domain of customer base analysis, probabilistic approaches from the ‘‘Buy ’Till You Die” (BTYD) model family represent the gold standard, leveraging easily observable Recency and Frequency (RF, or RFM when including also the monetary value) metrics together with a latent attrition process to deliver accurate predictions (Schmittlein, Morrison, & Colombo, 1987; Fader, Hardie, & Lee, 2005; Fader & Hardie, 2009). The simple behavioral story which sits at the core of BTYD models – while ”alive”, customers make purchases until they drop out – gives these models robust predictive power, especially on the aggregate cohort level, and over a long time horizon. Extended variants of the original models (e.g., Zhang, Bradlow, & Small (2015), Platzer & Reutterer (2016), Reutterer, Platzer, & Schröder (2021)) improve predictive accuracy by incorporating more hand-crafted summary statistics of customer behavior. However, including customer covariates is cumbersome and an approach to account for time-varying covariates has only just recently been introduced by Bachmann, Meierer, and Näf (2021) at the cost of manual labeling and slower performance. Even advanced BTYD models can be too restrictive to adequately capture diverse customer behaviors in different contexts and the derived forecasts present customer future in an oftentimes too simplified way
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#deep-learning #keras #lstm #python #sequence
LSTMs may not be ideal for all sequence prediction problems. For example, in time series forecasting, often the information relevant for making a forecast is within a small window of past observations. Often an MLP with a window or a linear model may be a less complex and more suitable model
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enges of RNNs to deliver on the promise of sequence prediction with neural networks. The applications of LSTMs achieve impressive results on a range of complex sequence prediction problems. But <span>LSTMs may not be ideal for all sequence prediction problems. For example, in time series forecasting, often the information relevant for making a forecast is within a small window of past observations. Often an MLP with a window or a linear model may be a less complex and more suitable model <span>

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