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on 30-Jun-2024 (Sun)

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Flashcard 7642780929292

Tags
#deep-learning #keras #lstm #python #sequence
Question
A [...] based MLP outperformed the LSTM pure-[autoregression] approach on certain time series prediction benchmarks solvable by looking at a few recent inputs only.
Answer
time window

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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A time window based MLP outperformed the LSTM pure-[autoregression] approach on certain time series prediction benchmarks solvable by looking at a few recent inputs only.

Original toplevel document (pdf)

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Flashcard 7642783026444

Tags
#deep-learning #has-images #keras #lstm #python #sequence
[unknown IMAGE 7104054824204]
Question
For example, if we had two [...] and one feature for a univariate sequence with two lag observations per row, it would be specified as on listing 4.5
Answer
time steps

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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For example, if we had two time steps and one feature for a univariate sequence with two lag observations per row, it would be specified as on listing 4.5

Original toplevel document (pdf)

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Flashcard 7642784861452

Tags
#recurrent-neural-networks #rnn
Question
The simple behavioral story which sits at the core of BTYD models – while ”[...]”, customers make purchases until they drop out
Answer
alive

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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The simple behavioral story which sits at the core of BTYD models – while ”alive”, customers make purchases until they drop out

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7642786696460

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
To forecast future customer behavior, our model is trained using [...] of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive discrete time periods
Answer
individual sequences

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
To forecast future customer behavior, our model is trained using individual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive discre

Original toplevel document (pdf)

cannot see any pdfs