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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Learning sequences consisting of single non-zero entry and remaining zeros
Pattern such as [1 0 0 0], [0 1 0 0], [0 0 1 0], [0 0 0 1] can easily be learned by using LSTM. We have created patterns where above mentioned vectors serve as the basis but we reveal one index ...
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LSTM input layer shape in Keras using functional API
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I am trying to implement LSTMs on drug data the shape of the data is given below also the model but it throws an error which is "Input 0 is incompatible with layer lstm_1: expected ndim=3, ...
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multivariate biLSTM text classification
I have made a model that takes vacancy data and classifies it with (bi)LSTM. The variables of the initial dataset are: positiontitle ...
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Normalization of possibly not fully representative data
I am trying to train a classification RNN model on a sequence of table medical data, but I stuck with the normalization problem. I realized that I cannot simply use MinMaxScaler, because of 3 problems:...
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Keras: ValueError: Input 0 is incompatible with layer lstm_1: expected ndim=3, found ndim=2
I am using Keras functional API to write an LSTM model but It throwing an error can somebody please help below is the code for the model the output shape is 65. I am using Keras 2.2.4 and TensorFlow 1....
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How an input data flows through an lstm layer cells?
I make this sec2sec NN model for the purposes of learning:
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Sequence multi-class classification only learns a few outputs
I have a multi-event delineation problem, where given a signal, I have an output with the same signal length. Something like 0011002200, where each unique number ...
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time series prediction training on multiple lags using tensorflow 2
I am just studying tensorflow 2. Here is where I learned time series training on multiple lags using LSTM:
https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
In this ...
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Keras RNN(LSTM) Loss Not Decreasing
I'm running an LSTM model to classify astrophysics time series data. I am attempting to classify each time series as either a black hole (1) or not a black hole (0). When I run my RNN, the loss is not ...
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Time series forecasting with RNN(stateful LSTM) produces constant values? Is there any solution out there?
I tried lstm but results were getting constant.
Same question as in the link
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Which output of a BiLSTM layer should be used for classification
I am trying to implement a BiLSTM layer for a text classification problem and using PyTorch for this.
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What kind of Neural Network should I build to classify each instance of a time series sequence?
Let's say, I have the time-series dataset below-left. I would like to train a model in such a way that if I feed the model with an input like below-right, it should be able to classify each sample ...
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Modelling feature interactions in LSTM network
I created a binary model from 28 features, each sequence is 10 samples long. I tried these two models:
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Time series forecasting when one of the series is known
I have a problem where there are two time series $\{x_t\}_{t \geq 1}$ and $\{z_t\}_{t \geq 1}$. These two time series are correlated for fixed time instant but uncorrelated with each other across time....
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Which One is the Best Way to Create Training Sequences for LSTM-based Class Prediction on Time-series Data?
Let's say I have time-series data in the following way. I need to create training sequences of a fixed length as an input to my LSTM model on PyTorch.
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LSTM for Stock Return Prediction
I am writing my masters thesis and am using LSTMs for daily stock return prediction. So far I am only predicting numerical values but will soon explore a classification style problem and predict ...
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Predicting out-of-sample time points with LSTM
I'm working on a time series forecasting problem using LSTM. The data is univariate and non-stationary. I followed the this tutorial.
The data is processed as the following: First, the difference ...
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LSTM long sequences reduction algorithm
suppose I have a large multi-variate TimeSeries dataset with very long sequences (~50k)
I want to reduce each sequence size to constant size for LSTM training.
I thought about splitting each sequence ...
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Neural network for time series forecasting with an auxiliary data
Lets say we have 2 data sets. First is the close price time series data set and we want to predict future values of it. The second is volumes of each price from the first data set and we do not want ...
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How to pass a sequence of 4 images into LSTM and CNN-LSTM
I got an assignment and stuck with it while going down the rabbithole of learning PyTorch, LSTM and cnn.
Provided the well known MNIST library I take combinations of 4 numbers and per combination it ...
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Time series classification with handcrafted features
I have a dataset with handcrafted statistical features such as follows:
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How to present longitudinal data to LSTM for multiclass prediction
I need to implement a deep learning algorithm to predict an ordinal value, called 'Entity', using longitudinal health records data. I read a few articles and guides but I couldn't find a clear ...
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How to Classify Game Stages Based on Bitrate Time Series Data
I need suggestions for my project and would be glad if you would give me a hand.
I have a dataset of frames obtained from the old-school game DOOM. Each frame in the dataset has the following columns:
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How to improve LSTM accuracy on multiclass text classification?
So, I'm trying to build a LSTM model to classify multiclass text label. The goal is to make a prediction about user rating (1, 2, 3, 4, 5) based on their review.
My hyperparameter is like this:
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Advantages of CNN vs. LSTM for sequence data like text or log-files
When do you tend to use CNN rather than LSTM (or the other way round) in classification or generation tasks of sequential data like text or log-data? What are the reasons for the decision and what ...
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LSTM with multiple entries for the same timestamp
I have a dataset where I have multiple entries for the same timestamp and I want to use LSTM to forecast the next timestamp given the previous 5 timesteps. From https://machinelearningmastery.com/...
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Pytorch: how to pass the hidden state between the samples in LSTM?
I am trying to boost the performance of a object detection task with sequential information, using ConvLSTM.
A typical ConvLSTM model takes a 5D tensor with shape ...
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Problems with Concatenating Embedded Categorical and Numerical variables for LSTM use
I am new to here and new to Deep Learning too, so apologies in advance for any ill formatted code or wordings. I have a data set where I track 4 variables with 2 categorical and 3 numerical fields, ...
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Bidirectional LSTM usage with sensor data
I am applying a deep LSTM network in order to classify time-series data from different sensors. In the field (energy) I often see the research using bidirectional LSTMs for forecasting. I don't get it ...
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Set seed for a Class that calls Keras Models
I have class that I use to optimize parameters of a Keras LSTM model. It is known that to set seed for keras, one must input the follow on its code. But what I'm not understanding is where to put it ...
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How to modify a Convolutional Neural Network architecture built for a univariate time series to multivariate time series?
I have built a CNN (in combination with a LSTM cell) that takes 1D time series-like data as an input and performs classification. I am obtaining a good performance, but the complete data has actually ...
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Does a 3-D input shape (time series with features) to an LSTM Model Evaluate the Label at each Time Step?
I have a problem similar to the one posed in this video:
https://www.youtube.com/watch?v=flMCYqIn3eg
In that video, she had a set of data on a number of individual debtors and needed to find out if ...
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Can LSTM be used to predict value as regression problem?
I have time-series data as shown below. Which model is generally preferred if grig_id is needed to be predicted? Is it possible to use LSTM with a sigmoid ...
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How to realize ARMA-GARCH using a LSTM model?
I am trying to model the returns of the S&P500 using a multivariate LSTM neural network to compare it to the ARMA-GARCH process. In order to do so,
I need to model the conditional mean of returns....
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Semantic analysis score as input to LSTM model for improving stock price prediction accuracy [closed]
I have created a univariate LSTM model that is predicting value of Open Price based on last 5 years opening price of a particular stock. I'm getting a decent accuracy. Now, I want to do sentiment ...
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PyTorch LSTM with varying time steps
Is it possible to create an LSTM in PyTorch where the time steps are varying? For example, heights where measurements are taken at various times. The data might look like this:
Person id
Inches tall
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Sentence embeddings with LSTM to classify the sentences is not working
I am trying to build LSTM NN to classify the sentences. I have seen many examples where sentences are converted to word vectors using glove, word2Vec and so on here is an example of it. This solution ...
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Treating Null Degrees of an Angle
I have a dataset that measures the flight details of objects, based on what action was performed. It looks similar to below:
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How can I convert my predictions to text after predicting using RNN?
I'm building PoS tagger for our language. I give tokens to the words and tags using Tokenizer(). Functions for word and tag are different.
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Determining Confidence Probabilities for Distribution
I have a LSTM neural network that predicts the number of items required in each room for the next time step. So for example for room A, B, C and D the model predicts
A -> 3 items
B -> 4 items
C ...
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Time series forecasting. How use future values
I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....
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Padding before or after truncation for stateful LSTM in Keras
I am training an LSTM on a dataset with variable timesteps (between 10 and 6000). Using the truncated backpropagation through time (TBPTT) technique, I am truncating the sequences to windows of 128 ...
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Correct LSTM model to predict shuffled data
For a year I've been collecting data from my RPi:
[0 core load, 1 core load, 2 core load, 3 core load, environment temperature, fan speed, CPU temperature]
Now I ...
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1answer
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Help improving time series prediction with LSTM on PyTorch
So, I am trying to use a LSTM model to forecast temperature data on PyTorch. I am relatively new to both PyTorch and the use of recurrent networks so I took a model I found on the internet to start. ...
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What is learnt by a model having multiple LSTM layers, each getting a slice of the same input data?
I came across the following architecture: -
Generation of training and testing data
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Output of a network to it's input, keras
I'm trying to create a neural network in keras for time series forecast. I've build a concept, and now I'm not quite sure if it is possible to implement using keras.
I have a potentially complicated ...
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What is the encoders job and how does it do that LSTM
I am studying LSTM-RNNs and ran into a problem! I am not sure what exactly the Encoder's job is, or better: how he does it?
I know that the Encoder is transforming the input in a way, that the Decoder ...
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One LSTM for two currencies or two LSTM one for each currency?
Suppose I am building an LSTM model for currency forecasting. Assume that I am working on two rates: USD vs GBP and USD vs EUR.
Should I build one LSTM model with input size of two features (GBP and ...
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Is LSTM or pretrained BERTForMasked LM usable for predicting changed word in a sentence using a small dataset? (2000 samples)
I have a small (2000 samples) dataset of newspaper headlines and their humorous conterparts where only one word is changed to sound silly, for example:
Original headline: Police <officer> ...
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LSTM Train accuracy decreases in training time during the epoch
I'm training an LSTM model for sentiment analysis on a text corpus. There's a thing that I believe is not normal because I never have seen it in training my models. At the start of the epoch, the ...
