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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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Multivariate Time series forecast deep learning

My Dataset: I have data for vehicles - mainly engine sensor data but also gps location, weather etc. The data is high frequency - every second. I have aggregated to 1 minute. I roughly have somewhere ...
Joshua's user avatar
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Input 0 of layer "sequential_2" is incompatible with the layer: expected shape=(None, 3, 3), found shape=(1, 3, 11)

I am trying to predict stock closing price using news sentiment analysis with the help LSTM neural network but after I do model.fit(). I am encountered with the error message: ValueError: Input 0 of ...
pankaj yadav's user avatar
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What is the shape of the hidden/cell state of convLSTM2D?

I am new to convLSTM2D and I understand how it works, however, I am confused about the shape of the hidden states at different epochs ...
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Why does forecasting with an LSTM yield better results with shuffling?

I first partition the timeseries data into train, validation, and test splits, without performing any shuffling. Each row is a window of ordered samples, so my training data might be shaped ...
MuhammedYunus's user avatar
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LSTM DEPLOYMENT

I am new to this but I need to deploy LSTM MODEL on Robotic Arm for my project can anyone kindly guide me as I have trained my model and tested it I just need to know how to deploy it?
BASIT Abdullah's user avatar
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How good are LSTMs in generalizing when learning curves?

I'm interested in the following scenario: I want to learn a mapping that maps a function to another function, i.e. I want to approximate a functional operator. If one is unfimiliar with operators one ...
ZenDen's user avatar
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Multi-step CNN-LSTM Encoder Decoder Model is not fitting well on peak values

I am trying to predict 4 values concurrently for next 24 hours n_lookback = 48 n_forecast = 24 I am breaking the sequences like this: ...
Hiba Rehman's user avatar
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Whats a suitable feature selection method for Time series data across multiple files?

My problem is basically a higher dimensional regression, where my input is (100 levels, 300 timesteps, 23 features) My goal is to build a deep learning LSTM model that finds which level the data ...
Youssef Badr's user avatar
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What to put for X_train, y_train when using it for unsupervised LSTM for anomaly detection?

I have a dataset with 5 features (excluding the date) [Result, Ward, Age, Facility, Resource] . The train dataset has non-anomalous data, and the test dataset will have some anomalous data. This ...
Chiken's user avatar
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User Behaviour Anomaly Detection

I´m trying to detect anomalies in the behaviour of users in an app. My dataset have several fields, but I think the most important ones are User Id, TimeStamp, and Event_name. So for example I could ...
Juan Pablo Pereira's user avatar
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tf.keras.layers.LSTM: where did all these parameters came from?

Why does this code... model = tf.keras.Sequential() model.add(tf.keras.layers.LSTM(9,input_shape=(6,1),activation='relu')) print(model.summary()) ... print 396 ...
BsAxUbx5KoQDEpCAqSffwGy554PSah's user avatar
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Call volume prediction using LSTM and GRU

Machine Learning call volume prediction using LSTM and GRU I am trying to predict the number of incoming calls using LSTM and GRU I have done all the data preprocessing but upon training the model I ...
Kuda Kulrider's user avatar
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Are formulas in the article incorrect?

I am learning about backpropagation in LSTM. I have been studying an article and watching two videos on the topic. The videos 1 and 2 repeat all the information from the article, but with additional ...
Тима 's user avatar
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I am training LSTM model for flood water level prediction. How to make the performance of the model better?

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Param Thakkar VJTI CS's user avatar
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Drum sound classification using RNN issues - help needed

I am new to the field of machine learning, even tho I have solid background in semi-related fields (am control system engineer by trade) and as a hobby project I wanted to work a bit with sound ...
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LSTM, different size for feature set and target

I am trying to build a weather forecasting model. X_train shape :(2970, 1, 9) Y_train shape : (3299675, 1, 4) I am following ...
Abhishek Patil's user avatar
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Keras Tuner for a (stacked) LSTM model?

I have a question about how to correctly setup a Keras Tuner model for a stacked LSTM model. What I have tried is the normal tutorial with a loop and the hp.Int() function to define the size of each ...
Barry's user avatar
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LSTM predicted negative values although the training data are all postive values

I am training LSTM to predict tree sap flow (30 min interval). I am keeping getting negative predictions, although the most of the predictions look good. I tried to use different scalers for ...
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Calculate AUC-ROC and AUC-PRC for an LSTM Model

I have the following simple Bidirectional LSTM model for a binary classification task: ...
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Machine learning model that takes multiple records as input to help predict the last

I want to create a ML model that is able to forecast the yield from a farm. My data source gives me data about the inspections from the field, but that is too much info to fit in 1 record, so there ...
Milan N's user avatar
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Adding sliding window dimension to data causes error: "Expected 3D or 4D (batch mode) tensor ..."

I wrote a pytorch data loader which used to return data of shape (4,1,192,320) representing the 4 samples of single channel image, each of size ...
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Uneven spaced time series data, any advise on how to approach?

In dealing with uneven spaced time series data, any advise what would be the approach ? data is ECG data to predict if the blood pressure Sys would drop -20% or 80% of normal. In the usual approach ...
curiosityfrown's user avatar
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Temporal mismatch

I am building a predictive model to determine risk for a disease over the course of a hospital stay. I am using medical records from a hospital electronic medical record database. The predictions are ...
healthydata's user avatar
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Do LSTM, GRU and Transformer models with less layers and units perform better than larger models when classifying short text sequences?

I am working with a Kaggle dataset with short Twitter messages as text input. I made a copy here. When testing LSTMS, GRUs, bi-directional versions of the GRUs, and the Encoder layers of a Transformer ...
Joachim Rives's user avatar
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Model Architecture for Time-Series Forecasting with Categorical and Multivariate Data

Context: I was looking at using an LSTM model to forecast the amount of gold gained for each of 10 heroes in a game of Dota 2, a MOBA game, as a base model in some type of model architecture. The game ...
DCRA's user avatar
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I can't understand why the validation and training loss almost constant and not converging?

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user731995's user avatar
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Connecting Flatten layer to Dense layer

I'm struggling with my neural network. In short, I need to recreate a model from anywhere on the internet, I've found a model that combines BiLSTM, LSTM and GRU. However, based on the error I got when ...
Tatiana Budanova's user avatar
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Can my LSTM model learn feature engineering on its own?

I have a timeseries dataset and I am training an LSTM model on it to perform multiclass classification. My dataset has 7 columns => x1,x2,x3....x7 And has 4 labels => f1,f2,f3,f4 Since I have ...
Rushabh Kheni's user avatar
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Activity Classification through LOG file

I have a big dataset containing logs/steps that the user performed on my webpage (for example: Clicking on a "Homepage" button, typing some text in the field, etc.) These steps are labelled ...
Aayushmaan Garg's user avatar
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LSTM Model for Multivariate Multi-Series

I'm looking to create an LSTM model to predict a certain label trained on multiple short-time series data. How would I go about doing this? Each time series has 10-30 time steps and 20 different ...
Chino's user avatar
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Can I use TimeDistributed layer for multiclass classification?

I have timeseries machine sensor data and I would like to predict when the machine fails using the sensor data. There are 4 Failure states and 1 Normal state, total of 5 classes. I am trying to solve ...
Rushabh Kheni's user avatar
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Aside from trial and error, how do I select the number of layers and unit counts for LSTMS, GRUs, and Transformer units for text and time series?

When deciding on the number of units and layers for text processing or time-series prediction I rely heavily on trial and error. First, I look for a reference or paper on the topic such as the white ...
Joachim Rives's user avatar
1 vote
1 answer
46 views

Why is my LSTM model not predicting well when predicting labels for a new dataset?

I have a 15 timeseries datasets with 25-30 columns and is labeled by following a complex formula applied on the 25-30 columns. When training, I split the datasets as training datasets and unseen ...
Rushabh Kheni's user avatar
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1 answer
69 views

Questions about hidden states of bidirectional LSTMs

I read this in an article about bidirectional LSTM: In bidirectional LSTM, each word corresponds to two hidden states, one for each direction. Thus, we concatenate these two hidden states to ...
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Which ML algorithm is suitable for a dataset that has seasonality and trend?

I have a small dataset from 2006 to 2023, I would like to predict monthly sales for the next year. This is my data: I already tried Prophet and NeuralProphet, but unfortunately they don't work well ...
Hadis's user avatar
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How do I shape my output data for a time series classification problem using LSTM

I am wanting to use an LSTM for anomaly detection on a multivariate time series data. Let's say there are n rows each corresponding to a timestamp incrementing by an hour and d input features and d ...
Vjs's user avatar
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2 votes
1 answer
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What is the difference between hidden states in RNN and Transformers model?

I'm very terrible at NLP and I have searched for these questions but didn't find any answer, my question is, in RNNs, there are hidden states to remember information for processing the next state, and ...
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Why not Back propagate through time in LSTM , similar to RNN

I'm trying to implement RNN and LSTM , many-to-many architecture. I reasoned myself why BPTT is necessary in RNNs and it makes sense. But what doesn't make sense to me is, most of resources I went ...
Amith Adiraju's user avatar
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Anomaly Detection in Log Data using LSTM

Problem Overview: I am currently working on a project involving anomaly detection in log data. The anomalies are defined by deviations from historical patterns. The log data has a simple structure: [...
Raj's user avatar
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1 vote
1 answer
216 views

Understanding the concepts of word embedding in GPT-2

I have a program that calculate the word embedding using GPT-2 specifically the GPT2Model class: ...
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2 answers
30 views

how to fix my increasing validation loss and decreasing training loss?

here is the code that got me this, please i need an advise on what to do to correct this. ...
Michael Oyeboade's user avatar
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48 views

Can I use lstm/autoencoder to cluster multivariate time series?

I have a multivariate time series of driving scenarios which has X,Y positions, speed, orientation etc. of the vehicles. Each scenario A, B, C, D etc. are of different lengths with different delta ts ...
SVP1194's user avatar
1 vote
1 answer
26 views

TensorFlow LSTM model with lower epoch loss, but higher average RMSE. How/why?

I am very perplexed by the lower loss but higher RMSE: Here's a newer model with better loss scores on the same dataset and many predictors: ...
user2205916's user avatar
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33 views

PyTorch input shape for text classification using LSTM

I have three sentiment classes: POSITIVE, NEGATIVE, and NEUTRAL, along with a dataset consisting of 3000 sentences and their corresponding sentiment labels (POSITIVE, NEGATIVE, or NEUTRAL). Each ...
PatelisGM's user avatar
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Forecasting of multiple related time series

I would like to know if there are any methods to forecast multiple time series that are related ? I heard of LSTM but of what I can see it's more to forecast one time series and not several at the ...
Pierre21's user avatar
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Does a RNN also need a 3-dimensional input vector for a "Point-To-Point" forecast?

I know that for many applications a RNN (e.g. LSTM) needs a 3-dimensional input structure with [Batchsize, Sequence_Length, Features]. My question is if you also need a 3-dimensional input vector when ...
PeterBe's user avatar
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LSTM Layer producing same outputs for different sequences

Currently I try to train on a multi-label language task with imbalanced class distribution. I have the following model, where I removed some of the feed forward layers to decrease factors in the chain ...
Thomas Christopher Davies's user avatar
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Lack of Variability in Predictions from Multivariate LSTM Model

I've been working on a multivariate LSTM model for time series forecasting, but I'm encountering an issue where the predicted output doesn't exhibit enough variability. The predictions tend to be too ...
Pavol Krajkovič's user avatar
1 vote
0 answers
37 views

Is it possible to determine the probability of each time sample to belong a certain class using gaussian distribution with Recurrent Neural Networks?

I'm trying to train a deep learning model that predicts the probability of each time sample in a two-component time series . In this case, I want the target tensor (Y) to be a probability value for ...
Kevin Vargas's user avatar
1 vote
1 answer
31 views

Out-of-Range Target Variable in Sequence-based Machine Learning Model

I'm encountering a scaling issue in a machine learning project. I'm predicting a target variable from an input sequence (and doing this for many). However, I've encountered a challenge where the ...
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