Questions tagged [prediction]
For questions about prediction of a certain quantitative or a qualitative value by an algorithm
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Mechanism of Prediction Readjustment in Supervised Learning and Role of Self-Attention in Sequence Data Relationships
In supervised learning, when the prediction deviates significantly from the expectation, how does it "readjust"?
And... LLMs are a subset of deep learning, just as generative AIs are.
Is the ...
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How much is the acceptable percentage for Random Forest in Landslides prediction?
RF had been developed to overcome overfitting in decision trees but in some cases RF still experiences overfitting in landslide prediction, which varies from 2% to 12%. How much overfitting is ...
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Is this a valid application of Autoencodeers/VAE?
I am trying to predict a spectrum (1D vector) from various scalar inputs which are known to be correlated. As the spectrum vector is very long (4000 points) it was suggested that I use dimensionality ...
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AI and Machine Learning Prediction Algorithms for predicting outcome results of Hypothetical poll
Can artificial intelligence and Machine Learning Prediction Algorithms assist in deciding the Outome Results of a Hypothetical Online Poll?
Poll: Selecting favorite American President till date.
...
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Recognize patterns within random sequences
I am familiar with ANNs as I studied them back in the days for regression and currently I'm working with CNN's for image recognition. But recently I was reading more about pattern recognition in ...
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What metrics are there to measure the similarity of two disjoint sets of points?
I'm training an AI model to predict where to place train stations along a train track.
Specifically, I want to feed my model some information about the train track. The model generates a series of ...
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SCINet: how does interactive learning work?
i'm having some trouble understanding how does the basic building block of a SCINet works. In the paper the author describes the SCI-block with the following figure:
In which $\phi$, $\theta$, $\eta$ ...
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Generalize and optimize a model for multiple time series
I have a physics equation that takes so much time to be solved computationally.
So the idea is to optimize this computational time with Machine Learning techniques. I've already generated data ...
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Purchase prediction - What minimum feature will "upgrade" A rule-based algorithm into an "AI" algorithm?
Let's say I have an algorithm that tries to predict if a given item will be bought or not, within an X timeframe, based on it's price and other attributes.
To Do this right now, I'm collecting ...
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Why "Good Model" that performs great on holdout validation data fails on production data
I have this binary regression model that has ~500 futures with an unbalanced dataset with the following results.
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How to handle out-of-bound values in Production data?
So I have this model but the data may vary. And it is virtually impossible to always have the values in bounds. If I do I`d have to use larger period leading to concept shift which is worse.
The ...
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how to manage the impact of Covid on building a machine learning model
I need your suggestions for using historical data to build a machine learning model for analyzing the market and build an AI model(tree based model/random forest or regression analysis) for setting ...
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What model can solve vector to vector prediction?
I am totally newbie into serial prediction.
I am think about which model or AI paradigm can be used to do vector to vector prediction?
For instance, [1,0,1] ^ [0,1,0] = [1,1,1]
Another example could ...
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Is there any way to train a regression model with negative values that is more stable?
I have a regression model where my target values contain roughly 60% negative values and 40% positive values. My model architecture includes a robert-large, 1 linear layer. I trained it after 1 epoch, ...
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How to make a proper approximation of Sine function with Neural Networks?
TL;DR;
How to build a neural network that properly approximates the sine function with different ranges?
Context and Question:
From this question I decided to use the Sergey's answer, however I used a ...
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Temporal Graph Neural Network for motion prediction
Temporal Graph Neural Networks have been used for motion prediction (or traffic forecasting) in the following recent papers:
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion ...
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time series analysis: predict number and type of service
I have temporal data regarding the number of customers who requested a specific service in a given period (month and year). Below is a small excerpt from the dataset:
Month-year: month and year when ...
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Which ML algorithm is the best for predict the next PRNG generated numbers?
I have a homework. The task is to decide, if the PRNG generated lottery is attackable/crackable or not.
Details:
Lottery:
There is a lottery game where you have to choose 8 numbers between 1-20 for ...
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What to predict in a limited transaction dataset?
I have been given a task with a real transaction dataset. The task is to predict something using either logistic regression or simple binary classification.
The columns are as follow:
Transaction ID
...
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Generating a dataset from data with "assumed" lables
I've got a task similar to the following:
Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those ...
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Should I train a neural network with data with or without a constraint?
I want to train a Neural Network (NN) using a dataset. I want to use the NN model as a prediction function in one algorithm. However, in the algorithm, any data that does not meet a specific ...
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Closed networks vs Networks with a removed delay to predict new data
I've come across two types of neural networks to predict, both from Matlab, the closed structure and the net that removes one delay to find new data.
From Matlab's app generated scripts we see:
% ...
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How can I predict the next number in a non-obvious sequence?
I've got an array of integers ranging from -3 to +3.
Example: [1, 3, -2, 0, 0, 1]
The array has no obvious pattern since it represents bipolar disorder mood swings.
What is the most suitable approach ...
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How to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$?
I would like to know what are the standard approach to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$. I use to see around that for this ...
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How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?
I have a question
I have had a dataset below
...
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What is a better approach to perform predictions of time-series several values ahead?
Suppose one has a time series (univariate or multivariate) and the goal is to predict values of these series several steps ahead. I see two possible strategies:
Create a model (recurrent, ...
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Has someone correctly predicted one of the variants of SARS-CoV-2 (like the Delta variant)?
Without any evidence, I have wondered it might be possible to predict the upcoming mutations of the COVID-19 virus. I am further assuming people did so.
So, has someone correctly predicted the ...
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What are the standard ways to measure the quality of a set of numerical predictions that include uncertainties?
I have a radial basis function that supplies uncertainties (standard deviations) with its predictions, which are numerical values.
This function is computed for a particular point by computing its ...
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Do the training and test datasets need to be equally preprocessed as one whole dataset?
I have developed, trained and tested an NLP model. It is persisted in a pickle file. The model contains the data preprocessing function that includes text cleaning and new features engineered with ...
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How to train a machine learning model with multiple attributes and one target value?
I'm working on a machine learning problem where I need to guess which customers will churn and which of them will continue to be customers.
I have $X_0, X_1, X_2, X_3, X_4, X_5$ and $X_6$ attributes ...
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Machine Learning in relation to personality and behaviors predictions
I am tasked with making a machine learning model that predicts personality traits and behaviours of children based on simple and interactive quizzes.
Currently I am lost and have no idea where to ...
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Predict time series from initial non-time dependant parameters
I'm trying to create an algorithm (neural network) that is able to predict a time series from a set of different parameters that are not given through time. Let's say I have a plane flying under the ...
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How to improve prediction performance of periodic data?
I have a 1 column dataset of $50 000$ points where 95% of the values equal $-50$. The data looks like the following: $$\begin{matrix}
\text{time} & \text{value}\\
1&-50 \\
2&-50 \\
3&-...
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Extracting information from RNA sequence
I am relatively new to machine learning, and I am trying to use a deep neural network to extract some information from sequences of RNA.
A quick overview of RNA: there is both sequence and structure. ...
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Multi class text classification when having only one sample for classes
I have a dataset of texts, each text was identified with an ID number. I would like to do a prediction by finding the best match ID number for upcoming new texts. To use multi text classification, I ...
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Can cryptocurrency charts be estimated using neural networks?
If I were to make a neural network that predicts the value of e.g. Bitcoin tomorrow based on the chart of the last month, would that work? Of course, 100% accuracy cannot be reached, but a success ...
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Which is the best algorithm to predict the trajectory of a vehicle using lat/lon data?
I'm using Kalman Filter approaches and I've just implemented the extended Kalman filter (EKF) with my object 2D trajectory. However, I have a mess of alternative approaches that may fit better like ...
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How to do early classification of time series event with small dataset?
I would like to build a real-time binary classifier that can predict an event of interest that is occurring as soon as it starts. These are electromyographic signals, and the event classification ...
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How to deal with predictions for data outside the range of the training dataset in neural networks?
I’ve set up a neural network model to experiment with predicting foreign exchange rates based on various economic data. The model learned fine and the test data is OK ($R^2 = 0.88$).
But I can't ...
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Predict next event based on previous events and discrete reward values
Suppose, I have several sequences that include a series of text (the length of sequence can be varied). Also, I have some related reward value. however, the value is not continuous like the text. It ...
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Root finding in Deep Equilibrium Models
In the Deep Equilibrium Model the neural network can be seen as "infinitely deep". Training learns a nonlinear function as usual. But there is no forward propagation of input data through ...
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How to perform prediction when some features have missing values?
Sorry if this is too noob question, I'm just a beginner.
I have a data set with companies' info. There are 2 kinds of features: financial (revenue and so on) and general info (like the number of ...
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How to use a NN for seq2seq tasks?
I am trying to make a NN(probably with dense layers) to map a specific input to a specific output (or basically sequence2sequence). I want the model to learn the relation between the sequences and ...
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How to train a model to predict the number of people at a certain bus stop before they cumulate in large numbers?
Each person probably uses an app that tracks his/her position periodically and sends it to our servers. What I want is to use these data to train a model to predict the rush hours of each bus-stop on ...
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Compressing text using AI by sending only prediction rank of next word
Is there any effort made to compress text (and maybe other media) using prediction of next word and thus sending only the order number of the word/token which will be predicted on the client side
i.e
...
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What ML algorithm should I use that suits this data?
What if I have some data, let's say I'm trying to answer if education level and IQ affect earnings, and I want to analyze this data and put in a regression model to predict earnings based on the IQ ...
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When is adding a feature useless?
I'm building a model, where, from a feature set A, I want to predict a target set C. I need to understand if another feature set B, together with A, can improve my model performances, instead of using ...
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Which machine learning approach can be used to predict a univariate value?
I have a stream of data coming in like below (random numbers 0-9)
7, 7, 0, 0, 8, 9, 2, 7, 3, 8, 2, 8, 5, 7, 0, 8, 7, 8, 5, 3, 2, 6, 1, 9, 5, 7, 5, 3, 4, 9, 1, 3, 5, 5, 0, 7, 7, 5, 2, 8, 8, 7, 5, 5,...
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How can I predict the label given a partial feature vector?
Most of the traditional machine learning algorithms need a feature vector of a constant dimension to predict the label.
Which algorithms can be used to predict a class label with a shorter or ...
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Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?
Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
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