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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Timestamp sequence classification
I am trying to classify a series of timestamps using RNN with LSTM. The data consists of timing information extracted from the uplink packets recorded during a website fetch. The dataset contains 100 ...
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How to shift predicted value to the next input value using neural network model
I have a dataset with three inputs: X1,X2,X3. I wrote a code to predict the next value of X1 in every 60 minute using lstm and I want to shift that value to the next input, then predict its next value ...
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How to input data to LSTM?
I have a labelled dataset (0 and 1) with two types of short length time series data (from day1-day5) as follows.
Type1 (...
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29 views
How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?
I am trying to use a LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
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Defining features in an LSTM [on hold]
I want to feed an LSTM model with $12$ different time series features. Now I want to know how I can implement this and what ...
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LSTM Predicting Trend with Forward Lag
I have trained an LSTM to predict time series data $30$ steps long with some imputation. The model I have trained has score t and an indicator random variable as features and tries to predict the ...
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Which algorithm to use for path to prescription?
I am working on a business problem in the commercial pharma industry. In the pharma industry, we have medical representatives (Reps) selling drugs to health care providers (HCPs). They frequently ...
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'tuple' object is not callable while reshaping training data using python
I have data csv file with three inputs names temperature, humidity, wind. Here I want to predict temperature value in every 60 minute using LSTM model.
Here I write the code to reshape the train . ...
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How to transform stock data for LSTM-based neural network
I am trying to classify stock returns using an LSTM-based neural network.
I would like to use closing price and volume as features (see below), but am unsure of whether I need to transform these (e.g....
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How many epochs is enough?
Is there any formula between the number of training inputs, the number of features and the number of epochs that's enough to learn the model?
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28 views
NLP - Identify Tagged Words
Please pardon me as the title might not be very accurate
I am trying to create a model that learns the word representation and then is able to predict word representation in another piece of text. An ...
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How X(inputs) and Y(outputs) go through the LSTM?
In LSTM animations or pictures I see X will go through the network as it's input, but I can't understand is X only the training ...
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How can we compute likelihood in recurrent neural networks?
Suppose that we have a recurrent neural network (RNN) with length $T$ for a classification task that generates an output at every time step which is a probability distribution over classes obtained by ...
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How to train a language model with bi lstm layers?
I am trying to understand how to train a LM using bi-LSTM in the case with "stack of bi LSTM".
In the case of forward LSTM, we just need to add a classification layer on the top of the last hidden ...
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How to standardize my data (Univariate Time Series Forecasting using Keras LSTM)?
Let be $X = (X_1,...., X_n)$ an univariate time serie. I would like to know how to standardize my data when I split it into train and test data. Let me explain you how I tranform $X$ so that I can fit ...
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Keras: Merged/Concatenated model perform worst than separate models memes recognition
I have a dataset of memes, and I'm trying to predict if a certain meme is sexist or not, using image and text together.
Right now I have two models, a ...
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Training multiple multivariate time series
I have just learned LSTM for one month, and I am doing a project that aims to train an LSTM model forecasting the taxi demand at "t+1" according to the taxi demand at "t", "t-1"... In particular, I am ...
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How to write a LSTM model with 3 dimensional X_train and Y_trains?
I have X_train and Y_train with [2160,24,3] dimensions. But when I try a simple LSTM like this:
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Keras error in dimensions when predicting
I have trained a Keras LSTM model and was now trying to use it for predictions but for some reason in is giving a dimensions error I cannot find. I processed the data in the same way as the training ...
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What are h(t-1) and c(t-1) for the first LSTM cell?
I know in a LSTM chain you should connect the h(t) of the previous cell to the h(t+1) of the next cell, and doing so for c(t). But what about the first cell? What does it get as h(t-1) and c(t-1)?
I ...
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How to write a code to display output value in every 60 minutes using panda python using csv file
I have temperature in a csv file. My file is updated with time . I have loaded this data to a pandas DataFrame. What I want to do is predict temperature value in next 60 minutes.
I changed the start ...
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How LSTM compare which information is important or not?
I am interested to know, if I have scaled my data between [0,1], and have a vector like [0, 0.001, 0.01, 0.1, 1], is that mean ...
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1answer
21 views
LSTM get next output with Keras
So I'm learning RNN, and tried to do a prediction LSTM, but I do not understand how the output works. I have this LSTM RNN:
...
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What DNN topology can I use to tackle a hierarchical multi-class classification problem?
Suppose that the sample set consists of labelled data, where each label corresponds to a class (say a sub-topic), and every class belongs to a group (a topic). The model should be able to predict the ...
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Error when checking input while running the LSTM model using panda python
I have a data set include with temperature, humidity and wind. Here I want to predict future temperature value in next hour.
I used LSTM to predict future temperature value.
But when I run the model ...
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2answers
33 views
Which performs better in time series forecasting, LSTM or SVR?
I have run LSTM and SVR models on various datasets having sample values in the range of 1-4000 and the MAPE obtained in SVR was consistently lesser than that obtained through LSTM. I was told the ...
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Time Series Classification with multiple rows per date
I have a time series data set with the lifecycle of 9000 different B2B sales leads. What I call lifecycle consists of a dataset with one registry per day for every different sales Lead identifier with ...
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Time Series Forecasting with RNNs
I'm attempting to develop a recurrent model to forecast the value one step into the future (i.e., $x_{t+1}$), given its history $(x_{t-h},\cdots,x_{t})$, where $h$ is a fixed hyperparameter for the ...
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1answer
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in TensorFlow 2.0, what is the different between LSTM and LSTMCell objects?
I am trying to implement an RNN in TensorFlow 2.0 (beta1). Looking at the layer functions (inherited from Keras) I found:
tf.keras.layers.LSTM
and
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Is Elmo equivalent to Fasttext+Bi-directional GRU?
From what I have read, Elmo uses bi-directional LSTM layers to give contextual embeddings for words in a sentence. So if I use a bi-directional LSTM/GRU layer over Fasttext representations of words, ...
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How to Reduce Overfitting of Deeplearning models on NLP tasks in unbalanced datasets?
I have a binary classification problem, where the number of examples belonging to Class 0 is 20% on average. And the rest 80% of examples fall into Class ...
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Difference between globalmaxpoolin1d() and attention layer
What's the difference between globalmaxpoolin1d() and attention layer?
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How to generate sequence of text(overall trend) by reading stock price
I would like to generate a sequence of text by reading stock price, this sequence text should contain describing the trend of the stock prices and trajectory.
There are two types of input sources, ...
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Stock price forecasting example with LSTM or GRU that beats a simple persistence forecast
There are lots of examples in the internet about how to do predict time series. However, they all suffer from at least one of the following problems:
tiny data set (like the "shampoo sales" with a ...
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2answers
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Keras input for multivariate classification with LSTM using current features and previous timesteps features and y values
I am working on a multivariate binary classification problem. What I want to do is to predict a binary classification given the features at the current timestep and the data (features+real ...
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How to write inputs append with time using panda in python
I have a data csv file include with three inputs temperature, humidity and wind. So in this csv file first input recorded at 6:00:00a.m.
But I want to start my time as 0 and then my second time is <...
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Generalization of RNN/LSTM/GRU… model
Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this:
to_predict (orange) was fed to the model, predicted (purple) is the ...
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Error propagation in Time series forecast with many-to-many multi-steps RNN/LSTM
I am trying to do a many-to-many time series forecast, which features an encoder-decoder model to predict with variable input and fixed prediction period. In my case, I want to predict for the future ...
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preprocessing : Predicting with Multiple+Multivariate+Multitrend time series data
I am trying to predict the value of a variable in a multivariate time series; of which I have multiple time datasets (one system = one dataset containing 10 variables in time and average 120,000 rows) ...
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how to write start_time= 0 and increment with 60 minutes to display y value (predict) continously using python
Here I have a dataset with three inputs, temperature, humidity, wind. I want to predict temperature value using these three inputs data in future at every 60 mins.
For the time code, here my first ...
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Help needed implementing Convolutional Sequence-to-Sequence Network
I am trying to build convolutional Sequence-to-Sequence network that takes inputs (satellite images) and predicts the next sequence of images. As a result, we can then predict the weather. I have ...
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1answer
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Understanding Layers in Recurrent Neural Networks for NLP
In convolution neural networks, we have a concept that inner layers learn fine features like lines and edges, while outer layers learn more complex shapes.
Do we have any such understanding for ...
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LSTM with fuzzy logic
I need to implement a fuzzy LSTM model for single time series prediction but i am stuck on the algorithm . could you please clarify it . i wanna open a discussion here . There is no Package on python ...
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What arguments should I pass to input_shape parameter of LSTM function in Keras?
My dataset has 2944424 rows and 6 columns. I am using an LSTM in Keras to forecast taxi demand. I am having problem with the input_shape parameter of the LSTM.
It ...
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How to shape input for multi-site multivariate time-series forecasting in LSTM?
Can anyone help me with how to shape the input for multi-site multivariate time-series? My dataset is something like:
One csv file for each time-step:
Each file contains 5 parameters (x,y, u-velocity,...
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LSTM -Detect single point in time series
I know that LSTMs can learn dependencies for many variables across many timestamps easily. I have used LSTMs to forecast the time series.
Now my Aim is to identify when the process is getting ...
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What machine learning model to predict a sequence of instructions from a matrix
So my task involves taking an input of variable sizes convolutional filters and predicting the sequence of instructions for a processor to implement that filter. Now the sequence can be of variable ...
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1answer
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How to identify new clusters that the training data has never seen
I have to identify the different operational states of a server. I have readings related to the different sensors of the server ( like temp sensor,fan speed sensor,job load sensor etc).The data I have ...
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Multi strep time series forecasting using daily data & LSTM
I have daily data from Jul 2017-Dec 2018 which makes 549 data points. I was trying to forecast Jan 2019 using Oct 2018-Dec 2018. In this example that is demonstrated above I can understand that in ...
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LSTM Producing Same Prediction for Every Input
So, I am currently working on a machine learning algorithm problem pertaining to car speeds and angles, and I'm trying to improve upon some of my work. I recently got done with an XGBRegressor that ...
