Questions tagged [sequence]

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How to organize data to use a recurrent neural network LSTM?

I am doing an internship in bailiff society. I have to create an IA model which can improve actions to perform, based on existing timeline of actions. I've already tryed some solutions (which did not ...
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Techniques for early binary classification from sequences revealed over time in a low data environment?

We have data with objects, each of which has a series of events. A series is 1-50 events, revealed over time (a few months). These objects have events come in at different times during a season, so ...
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LSTM as learned positional encoding for vor variable sequence length input

I'm solving a classification task on a time-series dataset. I use a Transformer encoder with learned positional encoding in the form of a matrix of shape $\mathbb{R}^{seq \times embedding}$. ...
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Forecasting on multivariate time series containing quaternions

I have a multivariate time series containing 3D position data ($x,y,z)$ and orientation data (as quaternions) obtained from motion sensors. My goal is to forecast the future position/orientation, and ...
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Regression sequence output loss function

I am fairly new to deep learning, and I have the following task. Based on an audio sequence of shape (200, 1024), I have to predict two sequences of shape (200, 1) of continuous values (for e.g 0.5687)...
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Train an LSTM on separate sequences of different lengths

My case is the following: I want to train a sequential classifier to recognize what action is being performed given sensors observations.My data consists of 10 executions of an assembling task for 10 ...
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What is the formal category of problem described by identifying consecutive occurrences of attributes in records?

Apologies for the garbled title, I'd really need to know the answer to the question before I could phrase it properly... Let's imagine I've got a data set of football(soccer if you prefer) match ...
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1D Sequence Classification using Circular Dilated Convolutional Neural Networks

I am working on a multiclass classification task on long 1D sequences. The sequence length may vary between $512$ and $512 \cdot 60$ timesteps, a slice of $100$ timesteps might look like this: What ...
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ML Modeling approach for Event data

I have this two dataset(image below).The one on the left shows events and the right is the alarm data. Goal : Using the two datasets, after any number of events, an alarm can be triggered.I'd like to ...
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1D Sequence Classification

Cross-post from https://stackoverflow.com/questions/71752744/1d-sequence-classification I am working with a long sequence (~60 000 timesteps) classification task with continuous input domain. The ...
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Given daily sequence of events with only event ID labels (alphanum strings), what algorithms can be used to detect sequences that are outliers?

For example, the data might be something like this: ...
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Predict indices of text using deep learning

I want to predict the start and end indices of text where a certain type of propaganda technique is used like smears, name-calling, loaded language etc. Some examples from the dataset are: ...
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Many to one LSTM, where some sequence values are known at prediction step

I have a time series problem, which I am modelling with an RNN (using LSTMs). The input contains a sequence of values x_0 to x_4, for predictions at positions n-k (where k is a configurable parameter -...
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Sequence Embedding using embedding layer: how does the network architecture influence it? [closed]

I want to obtain a dense vector representation of protein sequences so that I can meaningfully represent them in an embedding space. We can consider them as sequences of letters, in particular there ...
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How to predict data from sequence of sequences of variable size?

input data ...
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LSTM behaviour with return_sequences and TimeDistributed

I am trying different models for a classification problem with sequence data and variable sequence length, the below model predict all at once, and it achieve better results than other models, so I ...
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3 answers
110 views

Classification when the classification of the previous itens matter

I have a classification problem to solve, that seems to be common but I am struggling to find the name of this task and the best way to model this problem. Suppose I have a series of events that are ...
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How to compare different forecasting models over different time horizon?

Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons. The time series data is ...
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How does data shuffling work when LSTM is involved?

TIL that when using the LSTM layer, the states are remembered throughout the same batch. When using stateful LSTM, they can be even remembered outside of the batch. The first realization gave me a ...
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Optimal method(s) to monitor attention matrices when doing training/inference using BERT-type models from transformers

Our team is using BERT/Roberta from the huggingface transformers library for sequence-classification (amongst other tasks). We are looking for an efficient way to monitor the attention matrices so as ...
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Find a column by name in a row in scala spark

I have a Seq[Row].Each row is an Array of Struct.Struct has four fields: a,b,c and d all of which are String.The data in a particular row is something like this: [{"a":"ahahk",&...
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Text generation with deep neural network?

For my master's project, I have to build a deep learning model for text generation: the model learns on a set of sentences, then it generates new sentences based on those from which it learned. I ...
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Deep Learning Model to predict sum of Sequences based on flag value

I am trying to Predict Sum of the Sequence based on flag but my model is not able to converge. for each time stamp, include the first element in sum if second number is 1 in Sequence. Example ...
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1 answer
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Handle with very short and very long sequences with Neural Network

I am working on multi-class problem with sequences. My dataset is composed of sequences of data with different length. E.g. 1500 labeled samples: 500 datapoint belongs to class A, 500 class B and 500 ...
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1 vote
1 answer
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Seq to Seq modelling - ML Algorithms to use

Am new to ML. While I learnt the classical ML concepts like Linera regression, Logistic regression, Boosting and tree based techniques, now am slowly trying to learn Deep Learning techniques like CNN, ...
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How can I predict the last element of the fixed length=8 sequence after I get each element?

There are fixed length lists [X1, X2, X3, X4, X5, X6, X7, X8] like this. I have many lists like them from the past. In the future, I will get new element of current list on weekly bases. one new ...
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1 answer
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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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Create sequence for a Conv1D layer

Im studying the following tutorial on the Keras website and I'm trying to understand how to create a sequence for a Conv1D layer. This is their method: ...
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Time-Series analysis with small data set, but long sequences

I'm working on a time-series classification problem. There are 3 classes. Dataset consists of 6 sequences from each class (total 18 sequences). Each sequence is 19,000 in length. What are some time-...
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1 answer
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Find differences of two protein sequence files

I have two separate files A and B of more than 100000 records of protein sequences. Now I need to find the sequences that are in ...
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1 answer
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Random Sequence Predictions

Can machine learning algorithms predict random number generators. key A= 1 2 3 B= 4 5 6 C= 7 8 9 Example to catch a number sequence 4 8 8 I would select B C C That would give me 27 number combinations ...
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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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1 vote
2 answers
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Choosing a model for input: categorised, weighted sequence, output: binary variable

What would be an appropriate model for predicting a binary target variable, given a weighted sequence? Sequences will be reasonably short, typically between ~ 1 and 5 elements. Illustrated example Say ...
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4 votes
2 answers
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In sequence models, is it possible to have training batches with different timesteps each to reduce the required padding per input sequence?

I want to train an LSTM model with variable length inputs. Specifically I want to use as little padding as possible while still using minibatches. As far as I understand each batch requires a fixed ...
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1 answer
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What's the difference between sequence preprocessing and text preprocessing in Keras?

In Keras, we mainly have three types of preprocessing, i.e., sequence preprocessing, text preprocessing, and image preprocessing. However, for me, I think the meanings of the word "sequence" ...
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2 votes
1 answer
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How to train a model on top of a transformer to output a sequence?

I am using huggingface to build a model that is capable of identifying mistakes in a given sentence. Say I have a given sentence and a corresponding label as follows -> ...
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1 vote
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Machine learning for circular sequences

My data are sequences of real numbers $a_0,a_1,...,a_{n-1}$. The length of a sequence is fixed and equals $n$. Each sequence is mapped to a real number $y$ and I want to predict $y$ given the sequence....
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3 votes
1 answer
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Recommender Model for Human Action in Income Protection

Problem Domain I'm working on a project that involves building a model to provide recommendations on the next best step for Human supervisors to take on income protection claims. Income protection is ...
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How to use time-sequence data with "meta data" of single value per sample?

I'm, trying to estimate a fish weight by a time-sequence signal of the change in resistance when the fish goes through a gate with electrodes installed. When the fish pass through the gate there's a ...
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1 answer
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How to convert DNA sequences in FASTA format to OneHot Encoded Pandas Dataframe for Neural Networks?

DNA sequences in FASTA format look like: CATGCATTAGTTATTAATAGTAATCAATTACGGGGTCATTAGTTCA... I am trying to convert them into one-hot encoded data in a Pandas ...
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1 vote
1 answer
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Predict status of upcoming project milestones with intermediate activities

I have data of 100+ project data. Each project has about 175 sequential activities from start to end. There are approximately 7 key milestones between those 175 activities that we want to predict. ...
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1 vote
0 answers
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What is External representation of time in Sequential learning?

I am reading the literature on sequential learning and it is often mention that in order to handle sequential/temporal data, there two categories of work in sequential learning External ...
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2 votes
1 answer
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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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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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2 votes
1 answer
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Comapring hidden markov models

Given a set of sequence transitions, there are different orders of hidden markov models that can be fitted to a dataset. Is there any test to determine which is the best model for a given sequence ...
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1 answer
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what's this approach to spatiotemporal data named as?

I have some sequential data (e.g. audio, video, text etc.) and I am using this approach to classify sequences. I am sure there's a name for it, but I can't think of it: ...
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1 answer
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Similar algorithm to apriori to find unpopular sequential patterns

I am working with a dataset that looks like similar to this one but it is way larger (approx. 30.000 arrays): ...
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1 vote
1 answer
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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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4 votes
2 answers
664 views

Can bidirectional RNN use variable sequence length?

A bidirectional RNN consists of two RNNs, one for the forward and another for the backward sequential directions, which outcome is concatenated at each time step. Would this configuration restrict the ...
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1 vote
1 answer
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How and When features are attached to target label

I am using Mallet CRF library and having training set sequences like below. ...
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