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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Question about LSTM input
I am trying to use LSTM to predict user input but my question is how can you get the actual input of the user and let the LSTM predict it? I tried to check online but I dont see anything about it. I ...
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Is it possible to "link-couple-connect" certain inputs with outputs in a MIMO seq2seq LSTM model?
I have a seq2seq model with encoder and decoder as LSTMs which takes INPUT as the past 4 days of building data (weather data, 5 zones data like occupancy, internal loads, indoor air temperatures, and ...
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LSTM focus more on long-term memory
I have a specific use case where instead of learning the best LSTM model (least mse), I want a model which has more focus on long term memory (Esentially less decay in LSTM gates).
Which hyperparamets ...
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What do results like these imply in a LSTM classification problem?
I am training a LSTM network to learn from multiple time series, and the output from the network should be binary (or equivalently a probability score between [0, 1]...
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How to prepare data for multivariate prediction with irregular window size for prediction?
I have a dataset of different products and their possible configurations. I want to build a model which can predict the next part for the product given the previous part/parts. This model will be used ...
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How is the encoder state passed to the decoder (LSTM, Keras)
My understanding is that in the Encoder Decoder LSTM, the decoder first state is same as the encoder final state (both hidden and cell states) . But I don't see that written explicitly in the code ...
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Predict time series classes 15 min before
I have multivariate time-series as input and I need to predict an event ("active" or "inactive"). The occurrence of class needs to be predicted (at least) 15 minutes before it ...
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RNN for continuous, real-time learning without pre-training
I am learning ML and I'm trying to solve this problem
Create a rock paper scissors game where the AI is able to beat the player more than 50% of the time.
My initial intuition was to use an RNN with ...
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Why is my LSTM prediction is saturated and have bad prediction?
I am new to deep learning. Currently, I am trying to predict torque based on its past values using an LSTM model. There are two datasets (generated from a scaled test), one with wear and the second ...
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Best loss function with LSTM model to forecast probability?
I am working on disease (sepsis) forecasting using Deep Learning (LSTM). The sepsis data is EHR-time-series data. Where, the target variable is SepsisLabel. The <...
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Basic doubt on embeddings by BERT, LSTM
When we use Word2Vec, Its obviously a non contextual embedding because every word has a same representation. When I pass it to my LSTM, We say the hidden states are the contextual embeddings of the ...
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pytorchs LSTMs use of 'bias' and 'weight' strings
Hi I am new to RNN and have come across this the following implementation of Pytorchs LSTM, but I cant understand how (or why) the 'bias' and ...
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How to use multiple parallel inputs for time series forecasting -- Pytorch
I'm currently working with the ECG recordings of several patients. I have the amplitude of the ECG for around 48 patients over the span of one hour, and I want to be able to forecast their future ECG ...
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Need help diagnosing a training curve for LSTM-network
I am doing time series prediction using and LSTM-network. The dataset is divided into a training, test and validation set. The LSTM-model structure (number of neurons and layers), learning rate, batch ...
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Contextual Embeddings LSTM DOUBT
I have a simple doubt.
When we use Word2Vec, Its obviously a non contextual embedding because every word has a same representation. When I pass it to my LSTM, We say the hidden states are the ...
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Data augmentation for region based time-series binary classification with contained feature values
I am working on time-series problem where I have feature values for various timesteps that are fed to a LSTM deep learning model.
My features are all values within the range of [0, 1].
It is a binary ...
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Val loss initially decreases, then increases
I've created an LSTM model to predict 1 output value from 8 features. My loss constantly decreases and my val loss also decreases from the start, however it begins to increase after so many epochs. ...
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Which performance metric is used for sequential dataset?
I have a dataset which looks like this, I have built LSTM model to perform seq prediction
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Poor predictions on second dataset from trained LSTM model
I have a dataset of just numerical values, 8 input features(EMG signals of leg muscles) and 1 output (joint torque). I've created an LSTM model, because the data I'm using is time series, to predict ...
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Predicting stock price using stock news sentiment analysis
I was trying to understand how can we use stock news sentiments to predict stock price. I was going through the video claiming to utilize stock sentiment analysis for stock price prediction. The video ...
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What is the procedure for data preprocessing for time-dependent LSTM classifier?
I attempt a beginner level LSTM classification task with a time-series numerical data, but my task is finding changes in features over time (in which those changes describe the outcome or the classes),...
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Energy Data Disaggregation with a regression model/LSTM
i am currently trying to develop a model, which takes a timeseries of mains energy data from a household as an input and should disaggregate this mains data in to specific device energy usage which is ...
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Which time series model to choose?
I am new to time series forecasting. I have two very large dataset consisting of about 530k values obtained from a scaled dataset. The nature of both of these datasets are different. One of them has ...
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How to add an Attention Layer on Keras?
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I am trying to replicate a BiLSTM model that I read on an article, but I am having a lot of trouble implementing an Attention Layer. I am not particularly concerned that it has to be the exact same ...
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LSTM input size
I want to add an LSTM layer to my sequential model, Below is what I have done My input data has dimensions of 792 X 8. And I am extracting 5 features out of it. I am getting error "Input 0 of ...
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LSTM Input for features/timesteps and locations/features/timesteps
I have seen time steps, units, batches, samples, sequences, features and more terms seemingly as if they are self-explanatory and yet all describing only in total 3 input dimensions to LSTMs.
If I ...
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Usage of Word2Vec
Sorry for the basic doubt,
I would like to know if I can use my Word2Vec straight for classification without using LSTM. My assumption is it’s not possible because the ordering of the words will not ...
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LSTM model accuracy checking
Is this a good result?
How do you print the model accuracy (in %) from this graph?
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Transformers vs RNN basic doubt
I have a basic doubt. Kindly clarify this.
My doubt is, When we are using LSTM's, We pass the words sequentially and get some hidden representations.
Now transformers also does the same thing except ...
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does "unravelling" lstm units still mean one unit
I have seen images of lst and rnn units online, where they "unravel" the unit.
Is this only one, singular, unit?
If you have multiple units in a cell (layer), are both the cell state and ...
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Is an LSTM cell autoregressive?
I am currently writing some stuff up on Long Short-term Memory cells (LSTM), and stumbled over a question which I had trouble answering on the fly. The LSTM takes as input $h_{t-1}$ (besides the cell ...
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Multivariate timeseries classification for each group in a dataset
Let's say, I have the following dataset:
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There is less inputs received while giving the exact number of inputs required
I am testing an encoder decoder lstm model .
the training phase went well but the testing phase after building the testing model is giving me an error while trying to input to the decoder layer , here'...
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Confused in selecting the sliding window size for LSTM
I have a sequence of data that I want to predict the next 12 time-steps using a LSTM. I have already made it stationary and scaled the data between -1 and 1.
Now my problem is to understand which ...
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How To Shuffle Long-Short-Term-Memory Or Gated-Recurrent-Unit Layer Cells Operation?
As you know these types of layers operate side-to-side, and although could have been implemented in Bidirectional layer to operate on both forward and backward directions.
But is it possible to change ...
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Deep learning approach for calibration of raw data using reference measurements and a recurrent neural network (LSTM)
I am using Keras and R for my calibration problem. I have raw temperature time series data of a low-cost measurement device, which has a strong linear relationship with reference measurements of a ...
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What causes the loss to be nan and accuracy to not improve?
I am training a Bi-Directional LSTM model as follows, to predict the probability of a binary class based on a handful of observed time series.
The model is as follows:
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Inputting time based features such as day of week into Deep Learning Model
I am bit unsure on how to add time based features such as "day of week" or "hours" along with time-series features into a deep learning model (i.e LSTM/Temporal Convolution Network)...
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3DCNN-LSTM with MRI sequences of different sizes
I am working on a project involving the analysis of medical MRI images (3D images). I would like to create a classifier for the progress of the Parkison.
I currently have a dataset containing one of ...
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Why do you apply tanh twice to the hidden state to compute the output?
I've already seen different lectures on LSTMs and each time, I realize that there is one point that I've never really understood, so I hope that someone here can explain or give a hint as to why it is ...
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Temporal rows selection for Recurrent Neural Networks
I have a time serie $x_{1},...,x_{n}$ with a temporal step $\Delta = date(x_{i+1}) - date(x_{i}) = (i+1) - (i)= 1 \ day $. For each $i \in [\![ 1,n ]\!] $, I know that the value of $x_{i}$ depends ...
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How to get attention maps from simple LSTM models for Multi-variate Time Series Classification
I'm working on a task for multi-variate time series classification by using the simple LSTM model with several LSTM layers. The classification results seem fine and I would like to generate "...
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RNN basic doubt
Suppose if I have 2 sentences:
"My name is Alex"
"Alex is my name"
If I am using a RNN, After processing both the sentences, Will the final output vector be the same?
Because RNN ...
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Machine Learning Model to Translate an Input Time Series to a Target Time Series?
I want to train a machine learning model to translate input time series signals into target (ground truth) time series signals. I have thousands of input-target training pairs similar to the ones ...
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Train a unique model over multiple time series
I'm currently working in a project involving time series.
I have nearly 100 univariate time series (representing the performance of an engine of cars between 2018 and 2022). My goal is to forecast the ...
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In LSTM why h_t output twice?
According the LSTM design:
The hidden state (ht) is output twice (1 and 2 in the picture).
If they are the same, why we need them twice ?
Is there a different use for each one of them ?
According to
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Features and LSTM
I have a problem while developing an LStm model. I have 4 feaures that I want to use to make a prediction. When I test my model with a single feaure I get average results but when I test with all 4 ...
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Does an LSTM model use trend in features?
Does an LSTM take into account a trend in a feature? Or does it only see trends from the previous output (Y predicted)?
To illustrate, imagine we have a trend in feature A. In our problem, we know ...
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Multilayer/deep recurrent layer
I might be missing something, but I'm completely unable to find any reference about this topic.
In the literature, there are many references about RNN, GRU, LSTM, STAR and many other architecture that ...
