Questions tagged [lstm]

A Long Short Term Memory (LSTM) is a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.

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What does vanishing gradient problem exactly mean? [duplicate]

I'm currently doing some stuff with ML, and I created a LSTM model to recognize activities such as walking or running. I have read that LSTM has advantages over traditional RNNs due to addressing the ...
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Is masking needed for prediction in LSTM keras

I am trying to do sentence generator using 50D word embedding. If my training sentence is "hello my name is abc" here max words is 5. So my first training ...
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Dimension of Dataset Many to Many one to Many in LSTM keras

My understanding is In Many to Many dimension of if X is(batch_size,timestep,vector_size) and Y is ...
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Predict time series of parishable and seasonal product just with 1 year dataset

I want to predict the amount of demand for several types of fruit in a number of market with LSTM model.$ $ But I have a big problem, that I only have the dataset of one last year and because of that ...
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ML model type for making multi-step time-series predictions

Consider the following problem: making a prediction for 1 month based on 5 years of stock close prices. What would be the best choices in terms of model structure for this problem? I have considered ...
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Using pretrained LSTM and Bert Models in CPU Only Environment - How to speed up Predictions?

I have trained two text classification models using GPU on Azure. The models are the following Bert (ktrain) Lstm Word2Vec (tensorflow) Exaples of the code can be found here: NLP I saved the models ...
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What is unrolling in LSTM

In keras LSTM if unroll set False does it mean that output of current timestep is equal to input next time step?
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How to normalize data for LSTM?

For learning purpose I am making simple dataset for LSTM, which can predict next number in sequence. Here is my x value ...
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Using LSTM to predict next number [duplicate]

I trying to understand working of LSTM So I'm doing simple code to simulate its action. ...
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Can I use the predictions of an LSTM model as a feature for a gradient boosting regression model?

I have a transactional dataset of a retail company, for which I am trying to predict the sales on a monthly time interval, I have used an LSTM model with two features, the timestamp and the sales. I ...
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Do we need to truncate test dataset for seq2seq LSTM?

I am running a summarization model which uses a seq2seq biLSTM with an attention mechanism. It is a standard practice to truncate the input dataset during training to 400 - 500 tokens. My question is, ...
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Can we normalize both continuous and discrete numerical values

I have a sensor dataset with 16 features as numerical values (12 are continuous and 4 are discrete). I am using LSTM model to fit the data and do some classification. As both continuous and discrete ...
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LSTM dimension for time series

I read that LSTM needs a 3D input. I have time-series data containing 2000 rows and 5 columns. How does my data convert into the input [samples, time steps, features]? Thank you
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window_size vs time steps

What is the difference between window size and time step in LSTM for time-series data? I have gone through https://stackoverflow.com/questions/54235845/what-exactly-is-timestep-in-an-lstm-model and ...
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LSTM Sequence to One Regression

I'm trying to train a LSTM network for Sequence to one regression, but I'm having problems with my dataset, although I'm using the definition given by Mathworks here: Y - Responses: Sequence-to-one ...
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Time Series Forecasting: ARIMA\VARIMA vs Machine Learning \ Deep Learning

I am working on the development of a time series forecasting, and I have some doubts on the model I should use to achieve better results. PREMISE: Multivariate Time Series: my time series is a ...
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Padding - which values for standardized time series data?

I have different time series sequences with varying lengths and want to use them as input for an LSTM. My approach is to use padding to fill shorter sequences. Is there any best practice about which ...
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ML Algorithm for Online Sequence Classification

I am writing a program to classify API call sequences at runtime. At the moment I am using pytorch as my ML framework. Initially I thought this could be accomplished with an LSTM network, but from ...
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LSTM good test/validation performance but poor on unseen data for binary classification

I have 30k sequences of 8 letters that needs to be classified in X or Y depending on the relative position of letters in the sequence. The features are converted to numbers via a dict mapping and ...
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Confused about Language Model by biLSTM

*I posted similar question on pytorch forumhttps://discuss.pytorch.org/t/lm-by-bilstm-confusing-output-not-state-order/95629, but it is about pytorch function. Let ...
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Does LSTM provide online learning for streaming data (online parameters' update)?

I read something about LSTM and I noticed that the training is done on a training set and it is long-lasting. How to behave to predict new points if I have daily streaming data? Do I have to train the ...
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Multi-Class Multi-Label Text Classification With RNN

I am training a Muti-Label classifier on text data by using sigmoid activation and binary_crossentropy as suggested in many ...
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LSTM Keras - input shape and shapes of X and Y with model.fit (am I doing it right?)

a quick question, please could someone help, it would be greatly appreciated! I'm struggling with understanding the LSTM input and I hope someone here could help me and confirm if what I am doing is ...
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How to use lstm for clustered data?

I have a timeseries dataset of users with different profiles. I want to use lstm for predicting 1 day ahead of each user. My approach to the problem is first clustering users of same behaviour. And ...
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Does LSTM without delayed inputs work as a deep net?

I want to predict a multivariate time series. My time series is $a_1(t),...,a_{k-1}(t)$ and I want to predict $a_k(t)$. I use the following keras LSTM: ...
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Using hourly disaggregated data to predict aggregate daily counts through LSTM?

I have disaggregated person level data related to an infectious disease (COVID). I have about 4 months worth of data, which includes information like each person's sex, age, race, etc. What I ...
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How to use teacher forcing in training model using LSTM for time-series problem

I am new to ML. I searched a lot about using teacher forcing in LSTM. Without teacher forcing LSTM works well for time series problem but it does not predict well for future sample. Can somebody help ...
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LSTM model in keras (R) with time-dependent and not time-dependent branches of inputs

I am using keras in R. I am studying 600 stations. For each station two types of information are available. The first type is ...
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Autoencoder based anomaly detection: how to train AE also with outliers?

Suppose the data without labels, i.e., unsupervised anomaly detection task. The data are multivariate sequences, so the idea is to use LSTM based autoencoder (AE). However, typically AE-s for anomaly ...
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Linear Returns vs Log Returns to make a Forex time series stationary

I have a GBPUSD Forex time series. I am preparing the series as an input to LSTM. As a best practice for LSTM I am making the series stationary and I have tried both linear returns and log returns for ...
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Generating text using LSTM given condition vector

I know that you can use an RNN to generate text given the first few letters ...
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Visual attention on images in LSTM neural network - how to implement?

I have developed a bi-directional LSTM on images, and I want to get some kind of visual feedback on how the neural network is working, something that I think is called visual attention. I want to ...
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How to model properly sequential data when the output has to be used as part of the next input? Model off completely when it makes single mistake

I have time series data and am fitting a (LSTM) neural network. The time series data include let's say a brain wave (var1) as well as the previous state (prev_state) and I want to predict a state (...
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LSTM model convergence to a fixed state

I understand that this question has been asked previously https://stats.stackexchange.com/questions/253967/why-would-an-lstm-converge-to-a-fixed-state-when-generating-sequences?r=SearchResults&s=1|...
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LSTM for multivariate time-series classification with unequal timesteps

I am attempting to use RNN or LSTM for multivariate time-series classification of my data. A sample corresponds to an actor, a time-step corresponds to an action and a single action consists of many ...
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Is Predictions using machine learning algorithm like LSTM, Linear Regression, Transformers,etc. really a prediction?

When I came through the models like Linear Regression , LSTM , Transformer model a doubt was raised . we train a model using train values, ex:" model.fit(xtrain,ytrain, etc)", and in the ...
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LSTM vector sum of inputs as memory

I want to set up a long short-term memory (LSTM) network to have the vector sum of the inputs, which live in R^d, as its memory (ct). what is the required choices of the weight, and activation ...
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Univariate time-series prediction using LSTM

I am trying to implement the vanilla LSTM prediction model from scratch, but I am kinda stuck on getting output from the hidden state (ht -> yt). These are what I am doing right now: The plot of ...
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Using an RNN to generate a sequence with a static end point using TF and Keras

I'm currently learning about how to use neural networks while working on a project of mine. In the project I'm attempting to have a neural network create a path from a starting point (0,0) to an end ...
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Sequence sub matching for a big database

I have database (~1M) of 1D sequence (variable lengths). I want to be able to match a query sequence against the DB in real time. The query may be a sub sequence of one (or multiple) of the database ...
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Trying to increase the F1 score for an NLP data extraction problem

We have a problem reaching a decent F1 score when tackling this NLP data extraction problem. When given a group (i.e. Female dogs, male dogs) we want to extract relevant numerical data from a ...
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Application of Wavelet Transform and Differencing on Time Series Data (to denoise and remove seasonal adjustment and other trends)

I am working on an LSTM model to predict time series data (stock prices) and I would like an opinion whether to denoise my data or not before feeding it into the model. According to INVESTOPEDIA, ...
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Predicting sequence element based on the previous M and the following N elements

I have an array of sequences of equal length, each sequence contains 300 numbers (M=300). Each element in a sequence is a number from 1 to 9: ...
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Proper way to make Train/test split on Time-Series

I want to create a model with LSTM to predict a user the next purchase value. For this I have used I used a user's purchase history. I have created the model and it works well, but honestly, I don't ...
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LSTM high accuracy but poor generation performance

I'm writing a LSTM model for generating music (in particular drums). My model is based on these 2 models: LSTM text generator LSTM drum generator The model seems to work fine, it trains and I can ...
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LSTM versus Random forest for Time series forecasting

Would you use the same feature vector when forecasting with a LSTMN as a Random Forest? Say features like 'day of week' and 'hour'. Or does the LSTMN learn this by just remembering from previous time ...
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Time series dynamic model: can we also learn time varying models?

I would like to train multivariate time series models with time varying weight information (time-varying relationship between data and labels). My understanding is that for example, autoregressive ...
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Is randomly split dataset to train and test in lstm model reasonable?(human activity recognition)

I have build an lstm model to predict human activity recognition with dataset OPPORTUNITY. I did two experiment with different oder of processing as below, normalized the dataset with minmax scaler, ...
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Test scores are way lower than cross-validation scores

I split my Dataset with 80% of the data for training and 20% for the test in the context of a binary classification task with a very unbalanced dataset. On the training set I do a 3 folds ...
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LSTM performs poorly with monotonically increasing test set values never seen in training. Why?

I have a dataset of approximately monotonically increasing values (in a time-series). I am using keras and LSTM to train the ...

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