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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How to visualize ner dataset tagged using BILOU?

I have a dataset for ner which is tagged using BILOU tagging method and example of same is below ...
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Timeseries LSTM: does test data need to come after training data?

I have one single, very long time series. I want to train an LSTM to distinguish between two behaviours (A or B) at every timestep (sequence-to-sequence). Because the time series is very long, I plan ...
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How to make an lstm ensemble with different input shapes using keras

This is what I got so far for making an lstm ensemble with one model input for each of the lstm models and for the ensemble model and it works perfectly. ...
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which Model to apply on panel data where unique id has 6-8 records and total records are 2,000,000?

I am new to such panel data where I have multiple observation for same ID in different Quarter and I am not sure what kind of machine learning algorithm I can apply. I have data from Q1-18 till Q4-...
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Performing anomalie detection on a battery volatge using LSTM-RNN

I am trying to detect anomalies in a battery output voltage for one month. I have the next data frame, as it is shown the data is collected each minute for each day so I have almost 1420 sample per ...
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Regression with LSTM network: use multiple time series as input

I've spent a few days on this and am starting to think I'm missing the obvious solution as this doesn't seem like a very uncommon problem. As an example dataset: I have 100 measurements with each a ...
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Binary classification and numerical labels

I am trying to create a sentiment analysis model using a dataset that have ~50000 positive tweets that i labeled as 1, ~50000 negative tweets that i have labeled as 0. Also i have acquired ~10000 ...
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NER with LSTM - How to recognize person names that are not part of the vocabulary?

I am learning Named Entity Recognition and going through posts similar to this one: Named-Entity Recognition (NER) using Keras Bidirectional LSTM So the sentences are fed into the model as a sequence ...
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Predicting millions of sparse timeseries using them to help each other

This is a very general problem faced by different types of businesses. Predict the future behavior of customers over time. Imagine that we have 1 million customers with their own features over time, ...
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Regarding the application of RNN on new data

Suppose the two univariate time series $X_{1,T}=(x_1, x_2, ..., x_T)$ and $Y_{1,T}=(y_1, y_2, ..., y_T)$. The next step would be to train an RNN or LSTM with input $X_{1,T}$ and output $Y_{1,T}$, in ...
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Loss Nan: How can I properly implement a LSTM Time-Series model with a lot of parameters?

The Problem: I am very new to TF and Keras. I am attempting to train a time-series LSTM. When using only a few parameters as a test, the model seems to work fine. Once I increase the parameters to the ...
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How to use features with lags of different lengths in LSTM?

I'm trying to predict a time series, let's say I have 3 features and a target variable. I used the standard approach when feature lags have the same length, for example, 10. Then the size of my batch ...
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Derivation of HiddenState wrt Output of LSTM

I'm busy trying to understand the math behind LSTM RNN's. In most of the math tutorials that I've found the derivations (Backpropagation) don't consider a dense layer before the output, instead they ...
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Passing data to RNN with Sliding window approach

I having hard time with LSTM's and RNN so my apologies if this question sounds like a very basic question. I would appreciate if you can help in any way. I am trying to train my RNN with LSTMs, but I ...
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PyTorch: Predicting future values with LSTM

I'm currently working on building an LSTM model to forecast time-series data using PyTorch. I used lag features to pass the previous n steps as inputs to train the network. I split the data into three ...
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How to implement a Multivariate multi-site application in LSTM?

I am trying to make a multivariate multi-site classification LSTM model using Keras. I have followed this tutorial from Jason Brownlee: https://machinelearningmastery.com/multivariate-time-series-...
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Specifics of Input data format and processing in LSTM models

I'm busy writing a LSTM from scratch(just started) and I just want to clarify something. If I have a data shape as 1,10,2 which looks like: ...
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Batching data for LSTMs vs fully connected models

I've implemented an LSTM auto-encoder. It trained well, and does what I want it to so far. But, I think I've misunderstood something fundamental about lstms. In a simple dense network whose input ...
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Is my LSTM model overfitting or underfitting?

I am currently working on a project to classify comments text into 11 different topics, using a Bidirectional LSTM model. However, the loss curves confuse me as there is a deviation of the training ...
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Time-series multi-step generalization from single step model

I have built a generic stacked lstm model of the form: ...
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Please verify my RNN/LSTM multilingual multi class text classifier approach

Can you please verify my approach for a multilingual multi class text classifier using RNN/LSTM? The dataset consists of authors (labels, response) and their letters (input data). The letters can be ...
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Keras model with LSTM quantization aware training

I would like to run quantization aware training with a keras model which has an LSTM layer. However, just the LSTM layer seems to not be supported. Alan Chiao seems to suggest here that it is possible ...
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How to detect time for the future events in time series data?

I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence ...
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How to predict future prices with Keras LSTM time-series prediction model?

I have a trained and tested LSTM model which is meant to predict Ethereum close prices using all time csv data (24h steps). How do I now go about inputting an empty dataframe with future dates to ...
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Stock Prediction Problem With LSTM [closed]

Lately I was working on a LSTM code to predict future stock prices. I did not get a good result from that. Also I saw a lot of articles talking about stock market data is a random walk and cant be ...
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My Stacked LSTM seems to be doing worse than a shallower one

I started with a two layer LSTM (+ Dense Layers) and which was: ...
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LSTM validation loss always locked into zero

I have been working on an LSTM problem based on this Kaggle solution: https://www.kaggle.com/karanjakhar/simple-and-easy-aprroach-using-lstm And my dataset preperation is pretty much the same. The ...
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Data set compatability with LSTM's or convLSTM's

I am currently working on a data set which has the structure - frames x Number of Objects. Each object is of size 7x5 and the task is to classify each object into one of the 4 classes. I have been ...
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How far into the future can I forecast a time-series with an LSTM and strongly seasonal data

I am working on a Sequence-to-Sequence + Attention model for some time-series data. Now I have a really long time series, basically 40 years of daily observations for multiple sensors. The data itself ...
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How to choose the correct data's shape for time series with Keras

I would like to create an LSTM using Keas, which takes as input a multivariate time series. Following that example, we can see ...
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PyTorch: LSTM for time-series failing to learn

I'm currently working on building an LSTM network to forecast time-series data using PyTorch. I tried to share all the code pieces that I thought would be helpful, but please feel free to let me know ...
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Is a BiLSTM layer required if we use BERT?

I am new to Deep learning based NLP and I have a doubt - I am trying to build a NER model and I found some journals where people are relying on BERT-BiLSTM-CRF model for it. As far as I know BERT is a ...
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ML Algorithm or approach to solve timeseries selection

I am fairly new to ML and I'm working on a problem, but not sure which algorithm to choose. The dataset contains a set of incremental time-series events, in consistent and set intervals, with a list ...
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Concatenation of CNN and LSTM to model time of a series of images

I have collected a dataset consisting of around 30'000 heat maps of 80 users. The heat maps represent typing behavior on a keyboard and are just images with a resolution of ...
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Generate sentences from keywords by adding formal word

For example, I have a list of key word like : I , hungry => output: I am hungry or I , author , poem => output: I am the author of this poem. Can someone please suggest the simplest way to ...
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Build a corpus for machine translation

I want to train an LSTM with attention for translation between French and a "rare" language. I say rare because it is an african language with less digital content, and especially databases ...
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Pytorch Luong global attention: what is the shape of the alignment vector supposed to be?

I am looking at the Luong paper on Attention models and global attention. I understand how the alignment vector is computed from a dot product of the encoder hidden state and the decoder hidden state. ...
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How to scale features for LSTM?

I am trying to correctly scale data for LSTM. I have a set of features like this: x1(t-1), ... xn,(t-1), y(t-1), where the ...
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Predicting Bitcoin price with LSTM (and other RNNs)

In order to try what i learned about LSTM, i downloaded a simple bitcoin price dataset, and tried to make a network which predicts the bitcoin price by feeding a sequence of price history. Looking for ...
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Understanding projection layer for BLSTM

In many research papers there are 'projection layers' related to BLSTM layers. For example, from here: "we trained an 8-layer BLSTM encoder including 320 cells in each layer and direction, and ...
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Dropout after the Embeding layer

I am working on a classification problem. I am using pre-trained GloVe word embedding as input I wanted to know whether adding a dropout after the embedding layer makes sense at all? I have seen a lot ...
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strange behavior for validation dataset loss and accuracy

I'm training an LSTM model for a binary classification and using validation_split = 0.2 (in Keras), I see the following curves for the loss and accuracies of the train and test datasets. Here are two ...
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LSTM model prediction is almost constant

I am new to RNN and LSTM and currently experimenting with different settings. When trying to model time series data in absolute terms (predicted close price), I am faced with the following problems: ...
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Predicting Player Position from pervious positions and LSTMs

I am trying to use LSTMs to predict player positions in a field game. I try to overfit 8 slightly different time series. For this overfitting task I just use the positions of the players. A data ...
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Time series prediction with Lstm on patients data

My data includes different time series length (depends on number of exams each patient did) as well the interval between exams is different. How can I run lstm on this kind of data? (Where the ...
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How to account for rare events at different time intervals while using LSTM neural networks?

I'm working on an interesting sequence-to-sequence (regression) time series problem where some static features/rare events can change the behavior of future time series. The problem is a forecasting ...
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Transfer learning from great labelled time series data to one with low quality labelling

I have a source dataset containing outputs from a sensor per minute and have made extra effort to label them correctly for approx. 3 weeks. I trained CNN-BLSTM network on that dataset which classifies ...
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Oscilations in loss curve [closed]

I saw a similar question, but I think my problem is something different. While training, the training loss and the validation loss move around one number, not decreasing significantly. I have 122707 ...
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LSTM with return_sequences - “Training a model on multiple timesteps simultaneously”

So I'm following Tensorflow's LSTM/time series tutorial and there's something I don't understand. I do understand what happens with return_sequences on/off, however,...

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