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.
1,146 questions
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Is possible to assess number of multiplication and sumation of a DL in a separete way?
I am from old school and I developed a machine learning solution that uses only sumation subtraction and comparisons, no multiplications, divisions or special functions as sigmoid or tangh. However, I ...
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How does an lstm layer interface with a dense layer?
I am unclear how an LSTM layer would interface with a fully connected layer and what this would look like visually as per the puthon code below. I am trying to understand and visualize this code. I'm ...
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Time series predictions with LSTM
I have collection of TEC data.My data sample for example the day1,day2,day3,day4. Case1: I have the following task to do: Training by the consecutive 3 days to predict the each 4th day. Each day data ...
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Is Keras.utils.timeseries_dataset_from_array suitable for a LSTM for stock price prediction?
my goal is to create a LSTM that predicts one day in advance the price of a stock. My input data are XBTC prices along with technical indicators. These are the steps I follow: detrend the prices ...
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Is my Pytorch's LSTM underfiting
Friends here I want to ask about graph loss on my LSTM Pytorch modeling for stock price prediction, with like this is my modeling overfitting? for the results themselves are good, like this: MSE: ...
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No difference between multiple 1 step forecasts and multistep forecast with LSTM
Is it correct that there really is no difference between conducting a multi-step forecast with an LSTM and multiple 1-step forecasts where you update the data in between each 1 step forecast? The ...
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LSTM predicts the same value
I am implementing in PyTorch an LSTM model to predict if the closing value of a stock will go up or down in the next 5 and 10 minutes. Specifically, I am using 24 years of 5 minute data with 19 ...
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Adding context specific information to RNN/LSTM at current time?
I have a time series of values like below, where I'm looking at a history of sales: ...
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How do you create a model to track where a specific event starts and ends within a dataset?
I'm trying to automate a process where someone has to tag when an animal jumps from one platform to another platform. Currently, a manual review of the video is done to note at which frame the animal ...
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What is the difference between an LSTM layer with a timestep of 1 and an MLP layer?
I have been trying to understand the key differences between an LSTM layer with a timestep of 1 and a standard MLP (Dense) layer. Since LSTMs are often used for sequence data, I was wondering what ...
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1answer
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Why not back propagate through time in LSTM like RNN
I'm trying to implement RNN and LSTM , many-to-many architecture. I reasoned myself why BPTT is necessary in RNNs and it makes sense. But what doesn't make sense to me is, most of resources I went ...
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LSTM forecasting model highly dependent on random seed: how to tame this effect?
I have a time series forecasting binary classification model made up of several layers of LSTM or GRU cells. The training is early stopped in a given validation set. The thing is my results vary ...
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LTSM doesn't fit correctly
The accuracy after the training of my model is very poor. Why? Link for the example dataset: https://drive.google.com/file/d/1kSZNlR0AKgLNSITq_pVxXA6eBJ5nzB5Q/view?usp=sharing There are the inputs: ...
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Why does my LSTM only print out one day?
I made an LSTM for predicting stocks and it only predicts 1 day but I want to predict a range. ...
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What is the main role of cross-domain transformer encoder in htdemucs?
Now I am studying music source separation using htdemucs. As you know, in htdemucs, we use cross-domain transformer encoder layer instead of shared encoder/decoder layer which use BLSTM in hybrid ...
