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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Why does the forecasting of this LSTM model look like a steady line?

This is a multivariate multistep problem using LSTM NN model. I am trying to forecast one variable by means of the other variables. However, the forecasting output looks like a horizontal line. Kindly ...
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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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model has not yet been built

I'm making CNN-LSTM model for forecasting but I'm receiving this error : This model has not yet been built. Build the model first by calling build() or calling fit() with some data. Or specify ...
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LSTM low training/validation error but really bad predictions

I'm building a LSTM model to create an automatic drums composer. I'm following this post: LSTM Metallica I've built my model and done all the enconding, I was able to emulate the behavior of the ...
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Problem in input shape Keras-LSTM

I want to make a predictor using Keras LSTM model. I have a sequence of places visited. The task is to predict the last destination. I went through different examples but it seems I am not able to ...
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How respective gating functions are ensured in LSTM?

I'm studying the Hochreiter-Schmidhuber long-short term memory recurrent architecture. The overall idea, information flow and manipulation is clear, and it seemingly works, but what I cannot ...
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Time series binary classification [closed]

Which deep learning architecture and algorithms do you most recommend for time series classification problem? Of course LSTM, I am looking for state of the art papers.
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Difference between stacked lstm vs attention mechanism in LSTM

What is the difference between stacked lstm vs attention mechanism in LSTM? It seem to me that both produce the same context vector at the end. EDIT: From suggestion by @shepan6, the difference in ...
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Generating new sequences from given set

I got two classes namely positive and negative with 1500 samples on each a total of 3k. A sample sequence is like: ...
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How to include the other variables at t=t to predict the target variable with time lags also in LSTM?

I am having a training data set for a time-series dataset like below: ...
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LSTM (Long Short-Term Memory) network usecase

I am new to LSTM and trying to put in a real life implementation, but not sure whether LSTM suits well. The use case is as following: There are many warehouses in different regions. Hourly, trucks ...
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how to build lstm with functinal api?

I am having a time series prediction problem and the data set has 4 variables. My data set is like below: ...
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Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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ValueError: Error when checking input: expected the_input to have 3 dimensions, but got array with shape (14174, 1)

hope you're all doing good ! I am working on Automatic Speech Recognition with Python with the LibriSpeech Dataset. After preprocessing the audios data and applying an "MFCC featurizing" I ...
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1answer
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Using LSTM to predict next word performs poorly

I am training a model to predict the next word in a sequence, my data is comprised of Reddit post titles and is structured as: ...
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Predicting next element of a sequence given small amount of data

I have data of bank branches and amount of revenue they have generated in a month. The data looks like this: I am tasked to find the expected revenue for the branch for the next month using machine ...
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LSTM's for timeseries with additional regressors

I have a dataset consisting of the weekly sales of 3,000 stores over the past 5 years, and have constructed a LSTM to forecast the next year of sales, given the previous year of sales. At each ...
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Why are predictions from my LSTM Neural Network lagging behind true values?

I am running an LSTM neural network in R using the keras package, in an attempt to do time series prediction of Bitcoin. The issue I'm running into is that while my predicted values seem to be ...
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LSTM-Model - Validation Accuracy is not changing

I am working on classification problem, My input data is labels and output expected data is labels I have made X, Y pairs by shifting the X and Y is changed to the categorical value ...
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Streaming sequence detection (Binary Classification) LSTM/GRU

I am currently trying to implement a model which can detect a specific sequence according to the training data which looks like the following: ...
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24 views

How similar is Adam optimization and Gradient clipping?

According to the Adam optimization update rule: $$m \leftarrow \beta_1 m + (1 - \beta_1)\nabla J(\theta)$$ $$v \leftarrow \beta_2 v + (1 - \beta_2)(\nabla J(\theta) \odot \nabla J(\theta))$$ $$\theta \...
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LSTM Neural Network gets stuck in a specific state when trying to predict new states over many time periods

I have built an LSTM neural network for category, or latent state, prediction. The data is more or less of the form: x1 = continuos number from current record x2 = continuous number from current ...
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Concatenating Encoder hidden states in LSTM pytorch

I am implementing a seq2seq autoencoder in pytorch: Q1) While it is true that we can keep the encoder as bidirectional, but can we keep the decoder as bidirectional as well(does it make any sense) if ...
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Multidimensional time series regression

I’m new to time series forecasting and I’m trying to implement regression models using both ARIMA and LSTM for a multidimensional and multivariate time series. The samples are indexed by time, ...
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Can features that are the same in every sample contribute to learning?

For simplicity, let's say that I am monitoring 4 sensors for an ongoing metric. The first column is the sensor ID and the second column is the sensor type. ...
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How can I build a self-attention model with tf.keras.layers.Attention?

I have completed an easy many-to-one LSTM model as following. ...
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Loss function bounces back up

I'm training a very simple LSTM in PyTorch. It's a single layer, and I'm using it for multi-label classification with a BCEWithLogitsLoss. My batches are shuffled,...
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Does a CNN think things inside the filter are collocated aka dependent on each other?

I am running a 1D CNN on tabular data. The rows are data that I have are not sequential, that is to say they are not part of a time series or ordered string, which is why I am not using an LSTM. So ...
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N-grams for RNNs

Given a word $w_{n}$ a statistical model such a Markov chain using n-grams predicts the subsequent word $w_{n+1}$. The prediction is by no means random. How is this translated into a neural model? I ...
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Predicting point sequence in image

My training set is a set of images (either 3 channel or 1 ofc i use only one type of channel). And the labels are a sequence of points in a specific order that i want to predict from the images. I am ...
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73 views

Generating the right target for an LSTM model

Trying to explain my question on a simplified data set. Having the following dataset: ...
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HMM and its competitive alternatives

In Natural language processing, what are the major applications of Hidden Markov Chain (HMM), and what are the alternatives that usually can outperform HMM, is RNN and LSTM always the choice right now?...
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1answer
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Prediction Outputs from LSTM NN

I'm working with an LSTM network to predict the surface roughness due to biogrowth on ships. I've got a Network that fits the input data I have relatively well, the problem is when I'm using it to ...
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How to go about cooling load forecast for a district level dataset?

I am trying to build a high accuracy cooling load forecast for a district-level dataset over various time horizons. My data consists of a time series of cooling load and consecutive weather data. I ...
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Extracting vectors of FastText own model to use it on a NN

I have trained my own model of fasttext using the pretrained model of English available on their website with the next code: ...
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Is using LSTM correct for this?

I wish to predict whether the difference in Value and Growth returns is positive or negative for the next month. To do this, I have collected data of a few features(to be specific, Macroeconomic ...
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questions on multi-step air pollution prediction

I am trying to use RNN to predict the concentration of various air pollutants for the next 24 hours. The input data consists of 72 hours long and every hour owns 14 elements such as temperature, ...
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i am trying to one hot the inputs for the encoder and decoder layers in lstm? is this logical?

the follwoing code is a generator I am using to pass the training, for this generator i am passing questions and answers. the first 200 Q and A I am reserving for testing. The relevant part of the ...
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Trying to understand neural network's performance

I am trying to build a RNN for classification and below is the layout of the network ...
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RNN: Multiple inputs per time step with categorical variables

I am trying to a build RNN model to forecast daily sales for several different cities and different product segments (categorical features and multiple inputs for each day) along with numerical ...
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1answer
72 views

pandas TypeError: 'range' object cannot be interpreted as an integer

I'm following this link for time-series forecasting. While splitting data set to create the data for the uni-variate model this ...
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What would be the mse (mean squared error) of my scaled dataset on the original scale?

I build an LSTM model on a standardized dataset using sklearn's MinMaxScaler. All values of the dataset are between 0 and 1. Features and target variables were standardized between 0 and 1. I achieve ...
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Predicting parameters of simple configured trajectories using RNN

What I'd like to do: predict orbital elements given an input observation sequence in 2D, that is $$input = X = [position_{t_{0}}, position_{t_{1}},\ ..., position_{T}]$$ $$output = y = parameters\ ...
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What is a good accuracy level for a chatbot in keras?

I have build the model and despite the level of accuracy at 0.87 the answers to my questions are not good enough. And somehow I am not getting the 'EOS' tag, making the answers go 20 words and its ...
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Should LSTM data be a sequence?

let me explain what I want to do, I want to predict the trend of the price of something (1 if it increases in the next hour and 0 otherwise). I have gathered tweets about that and grouped them in ...
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Definitive Values in Confusion Matrix

I built a convolutional LSTM model for the classification of 4-image time series. I used n keras ConvLSTM layers, followed by a time-distributed flatten and a few dense layers, finalized by a dense ...
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26 views

static and dynamic data in clinical trials

Hi everybody and thanks in advance for those who will help me for this problem. I have multiple data regarding patients involved in a clinical trial and my goal is to predict their death/non death. ...
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Using RNN to predict future power usage

So for each user in a file I have their average power usage value every hour for 40 consecutive days. I need to predict their power usage during next 10 consecutive days. I am new to RNNs (I have ...
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Why is my LSTM is working best with batch size of 2 and no hidden layers?

I am building an LSTM for price prediction using Keras. I am using Bayesian optimization to find the right hyperparameters. With ...

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