Questions tagged [lstm]
LSTM stands for Long Short-Term Memory. When we use this term most of the time we refer to a recurrent neural network or a block (part) of a bigger network.
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Is it appropriate to utilize LSTMs for multivariate binary prediction on a timeseries by sliding block-by-block vs row-by-row?
I am trying to implement an ML algorithm for multivariate regression on a list of several timeseries. There are hundreds of timeseries, each one millions of rows long. There are 13 features, and I'm ...
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GPU running out of memory for ConvLSTM model in Pytorch
I'm trying to replicate the bidirectional convolutional LSTM proposed in Xiong et al. 2017 to predict crowd count density maps, but I'm running into memory issues during the training. This is what I'...
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Using LSTMs for Predicting Targets with Known Feature Vector
I am trying to use an LSTM to predict the consecutive "offset" calibration values for an instrument. These offset values have previously been shown to be well correlated with a pair of ...
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LSTM not recognizing increasing and decreasing patterns
I am struggling to get an LSTM to recognize different patterns in sequences. As a very simple example, I would like my LSTM to recognize and predict both increasing and decreasing sequences. For ...
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Understanding Divergence in LSTM Units with Zero Initialization and Shared Weights?
I'm trying to understand how LSTM units can diverge over time when they start with zero initialization and share the same weights. Here are the key points of my confusion:
Initialization: All units ...
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using a 2d matrix as a feature input to LSTM / RNN models
i am building an LSTM model to predict the combination of items that will be sold at a store level on a daily basis. Please note, this is an exploratory model and i have a good idea about the ...
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deep learning performance stuck
Task
Multivariate time series forecast. I have one variable to forecast - and many explanatory variables. I want to train a model and then forecast the current period. This is for anomaly detection - ...
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Probability distribution for lstm and mlp
I have total of 6300 samples, 5800 of which are training data, and 500 of which are testing data. We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of ...
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Implementation of CNN-LSTM for multivaraite time series forecasting
I have recently completed the course on TimeSeries from Coursera - Deeplearning AI, and was trying to replicate the results of an open-access research paper (...
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How BPTT updating the weights while input are varing
how the RNN gets trained(BPTT) when the input size is varying because to update the weights the network has to be stable right
please reply on this
Thanks in advance
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Difference between LSTM and MLP
My teacher said me that:
We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of training process, prediction accuracy and learning ability.
Anybody ...
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Semantics Building In LSTM-Based Models - How does a LSTM is able to extract and represent long data using just one value (long-memory)
How does a LSTM is able to extract and represent long sequences with data while using just one value (long-memory / LM) to maintain all this information?
If multiple value were used, it could be ...
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How to include variable input shape in convLSTM2D?
I am classifying time series of 72x72 images in 4 filters (just like RGB). Things work well ...
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Multivariate Time series forecast deep learning
My Dataset:
I have data for vehicles - mainly engine sensor data but also gps location, weather etc.
The data is high frequency - every second. I have aggregated to 1 minute.
I roughly have somewhere ...
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Input 0 of layer "sequential_2" is incompatible with the layer: expected shape=(None, 3, 3), found shape=(1, 3, 11)
I am trying to predict stock closing price using news sentiment analysis with the help LSTM neural network but after I do model.fit(). I am encountered with the error message:
ValueError: Input 0 of ...
