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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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LSTM state-of-art

Can you advice me a good paper which talks about the LSTM's state-of-art? I have already searched on google but I have not found anything interesting Thank you very much
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Is it possible to make LSTM model with 4dimension shape?

Hellow, wizards. I have time series data including sevaral days. I try to predict a grade of tomorrow, which is range from 0 to 100. And I assume that this grade depends on 3 time-series independent ...
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Feeding Embedding Vectors With Time Series Data to LSTM [on hold]

I have daily sale data of some retail products on a three year span and I want to build a model that can predict future sales for all of these products. While looking for a way to forecast multiple ...
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Keras LSTM for timeseries forecasting help with error messages [on hold]

I am trying to understand how to use keras for supply chain forecasting and i keep getting errors that i can't find help for elsewhere. I've tried to do similar tutorials; sunspot forecasting ...
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Number of Hidden Layer Nodes in Recurrent Neural Networks

There's already a decent discussion on how to select the right number of hidden layers and hidden nodes in a feed-forward neural network: How to choose the number of hidden layers and nodes in a ...
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Time-series classification of Kinect data using Keras

For my PhD project I recorded using Kinect and Myo 11 people performing Cardiopulmonary Resuscitation (CPR), repeatedly doing chest compressions to a manikin (one person per time). I collected in ...
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Time Series Forecasting RNN: Difference Between Masking & Excluding Rows

Suppose you have missing values in a time series E.g. : t1 x1 y1 t2 ? ? t3 x3 y3 t4 ? ? t5 x5 y5 You are trying to forecast this time series using a recurrent ...
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Bias vector regularization in LSTM layer

Are there any scientific papers or articles on use of bias vector regularization for training LSTM models ( I am using Keras: https://github.com/keras-team/keras/blob/master/keras/layers/recurrent.py#...
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How to create dataset for speech recognition using librosa [closed]

I loaded the audio using librosa and extracted mfcc feature of the audio. I now have array of shape (20,N). How do I feed this as input to LSTM to predict?
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How to train a RNN language model?

I want to train a RNN-based language model from https://arxiv.org/pdf/1409.2329.pdf for next word prediction. How to split the sentences from the dataset into input and ground truth during the ...
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Keras LSTM Long Term Dependencies

I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras. In Keras the input data structure for X ...
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What is the initial state of the tf.contrib.rnn.LSTMCell? [closed]

Does tf.contrib.rnn.LSTMCell assign itself an initial state of zeros or is it random for each batch or per complete run through (if I run the model twice will it have the same initial state both the ...
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Batch size stateful LSTM in Keras - how to evaluate with just one sample? [closed]

I'm using Keras to classify time series of 1000 timesteps containing one feature. In keras the input shape of an LSTM is defined as (batch_size, timesteps, features). So my input is f.e. (32,1000,1), ...
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Variation in LSTM Model based on seeds

I am working on a time series project. The results of LSTM model which i am using varies a lot with the variation in seeds. I am wondering how can i make that model stable. Currently to get the ...
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Totally confused about input_shape dimension of stateful LSTM Network with keras [closed]

let's say I've got 2500 time series of class A, and 2500 time series of class B, both 1000 time steps long. There is only one numerical feature per time series. I want to train a LSTM using Keras to ...
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Can't get a Keras model to overfit [duplicate]

(Full disclosure, this is a follow-up to this question, which wasn't completely answered on StackOverflow) The input dataset is a time series of some stock price movement, but it might as well be ...
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What is the recommended maximum number of time steps for RNN or LSTM?

More time steps incurs longer training time, which above certain limit becomes impractical. What is the recommended maximum limit of the number of time steps for RNN or LSTM? I'm using a powerful ...
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forget_bias interpretation in tensorflow

In Basic LSTM cell of tensorflow there is an argument named forget_bias. From the documentation of ...
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1answer
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image caption generator

I see two models of image caption generator online: In the above model, the first LSTM cell of decoder takes the entire image as an input. In the above model, all the LSTM cells of the decoder take ...
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LSTMs and Opening/Closing Brackets

I'm training a character-level LSTM to generate molecules using the SMILES system. Each molecule is represented as a string of characters, looking something like this: Cn1c(Nc2c(Cl)ccc(CNC(=O)C(C)(C)...
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Which are the benefits of using ARIMA vs LSTM for time series forecasting? [duplicate]

I have already read this question: https://datascience.stackexchange.com/questions/12721/time-series-prediction-using-arima-vs-lstm but I want to know in which circumstances is better ARIMA and in ...
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Way build a ML model with dynamic dataset have dependencies between feasuers- python or node

I need build a model has to predict for the director of a treatment institute What is the recommended treatment for a new patient according to his Personal Information and Difficulty diagnosed: the ...
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how the long term memory works in an LTSM network

I am trying to understand how lstm networks work in particular the long term memory aspect. For example if we have a very long paragraph and at the start we have someone's name, Bob for example. Bob ...
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Backpropagation through LSTM and MLP

For didactic reason, I am currently implementing in numpy an LSTM network for classifications. I need to add on top of the LSTM another fully connected layer, because I don't want the output to have ...
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1answer
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LSTM - random and always-different time between data measurements?

I am working with a time series problem where the time between two data measurements is random, and I am trying, without luck, to find an LSTM architecture that can handle this. A very simplified ...
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1answer
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Selecting number of time lags for input in LSTM networks?

I know from theory that LSTM are meant to selectively capture long and short term dependencies in a sequence. I'm trying to implement LSTMs for a time series task and I notice that a lot of tutorials ...
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Use LSTM to predict large number instead of poboabilities

I want to predict datasets like this I'm using Keras' LSTM: Not like a classification problem which returns probabilities, this model needs to output the exact value depending on input giving to it. ...
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How to encode text and categorical variables together?

I have two groups of texts that are very similar (e.g. reviews written on fridays and reviews written on mondays), and I want to build a LSTM that can classify them into positive and negative reviews. ...
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1answer
28 views

the appropriate batch size for ltsm

I am new to machine learning. I am looking at using time series data with a recurrent neural network in particular LSTM. My question is to do with batch sizes. As I understand LSTM have long term ...
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27 views

About RNN with variable length output vectors

I have several thousands samples with equal number of features (5000, they are time dependent) and I would like to predict of vectors with variable length. 1) I'm beginner in RNN, and I'd like to ...
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How to get the same output on each training iteration of neural network?

I'm a neural network noob and am trying to use LSTM(Keras,Tensorflow backend) for predicting time series data. Every time I train the network, I get a very different set of output values even though ...
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2answers
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Is there any relation between number of hidden layers in a neural network and Performance?

I need to know if the number of hidden layers effect the performance and accuracy of a neural network, in other words does the increasing in the number of hidden layers of a neural network increase ...
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How to split data for stateless LSTM in Keras? [duplicate]

I have been thinking about the way of splitting the data into training, validation and test sets for the stateless LSTM. For me, the intuitive way is to arrange the original data into the 3D form (...
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How to diagnose loss curve not converging?

I am trying to predict remaining useful life (RUL) from temporal data with multilayered LSTM and obtaining the following curve: Looks like after first several epochs performance stops to improve and ...
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1answer
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How to get consistency in neural network and eliminate possibility of NaN values?

I'm using a neural network(Keras,LSTM) for time series regression. Whenever I run the network, I get different outputs for the prediction. This is presumably due to the randomised weight ...
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LSTM : multi-step multidimensional multivariate multi-site timeseries forecasting [closed]

I'm working on a project in which i'm trying to do a pollution forecasting. I googled around and found that LSTM is a good candidate for this task, however, I'm still struggling at how to adapt it to ...
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1answer
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Forecasting algorithms for incomplete time series data [duplicate]

I want to forecast the demand of each SKU in my warehouse every week from the history transaction that I have collected. The data contains brand, product type, SKU, quantity, date(per day), price. But ...
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How to paraphrase and augment training data for a question answering ML model?

I have only 50 question, answer pairs in my training data, where each question represent a unique intent. However, the training data is too small to build any meaningful ML model. What are the ...
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1answer
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LSTM output dimensionality

I am new to LSTMs. When reading the papers and websites about LSTM architecture, there is something I do not get. As I understand it, a single LSTM layer can have multiple LSTM cells (just like a ...
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How to apply dropout in LSTMs?

Dropout in fully connected neural networks is simpl to visualize, by just 'dropping' connections between units with some probability set by hyperparamter p. However, how dropout works in recurrent ...
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Reduce the effect of excessive zeros

I am working on an autoregression problem where I use sequential LSTM. My target is well defined, but I think I am facing a problem with the features. As the features were non-stationary, then I ...
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Getting over bid-ask bounce

High-frequency financial data is subject to bid-ask bounce. Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in the form of ...
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Limiting regression accuracy

I am currently using a LSTM Network to solve a regressional problem. The goal is to predict the payload mass using various time series as input data. For our intents and purposes estimating the mass ...
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38 views

Zero inflated binomial for excessive zeros

"What are some tricks for dealing with a zero inflated response variable when tackling a machine learning regression problem?" Answer : "One of the easiest and most intuitive methods is to run a ...
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2answers
56 views

Deep learning methods with seasonal data

I have been building an LSTM model in Python which will predict the number of passengers arriving at a station in the next 15 mins. My dataset is arrivals at the station every 15 mins across a 50 day ...
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1answer
479 views

Time steps in Keras LSTM

My understanding of time-series LSTM training is that the recurrent cell gets unrolled to a specified length (num_steps), and parameter updates are back-propagated ...
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3answers
138 views

Dealing with excessive number of zeros

ipdb> np.count_nonzero(test==0) / len(ytrue) * 100 76.44815766923736 ...
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2answers
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Why is LSTM so ineffective at this ridiculously simple sequence?

I am trying to predict the the sequence $(0.1, 0.05, …, 1.1)$ from the sequence $(0, 0.05, 0.1, …, 1)$. I thought this would be the only item in a toy dataset. Now I implemented a long-short term ...
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Adding noise to time series data to increase training data

I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...
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What are paralell training and attention mechanism?

I read a quite interesting paper here: http://hanj.cs.illinois.edu/pdf/kdd18_cyang.pdf Accordingly, the basic idea is to combine clustering and churn prediction so that it can imply some insight from ...