Questions tagged [multivariate-distribution]

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Bivariate Outlier Detection

I'm trying to find outliers in a bivariate data set. One of the input variables is the amount of time that it takes a user to do a task in our system in seconds (rounded to integer). Most interactions ...
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Comparing multiple multivariate datasets

Take the two datasets below: default rate state age income asofdate 10 Texas 55 100,000 202309 14 Texas 35 97,000 202309 18 Texas 55 95,000 202308 22 Texas 35 95,000 202308 8 New York 21 55,000 ...
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Multivariate Time Series Forecasting of only 1 of the variables

I have a dataset that has time series data in the following format - 1 column for the date, 5 dependent variables, 1 independent variable (need to predict) I want to see if the dependent variables ...
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Adressing uncertainty of a spatio-temporal multivariate timeseries with random temporal gaps

Imagine there are multiple locations of interest from where water samples are gathered manually. Each sample is immediately analyzed, converted to a numerical value (a real number) and fed into a ...
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Fit a bimodal histogram with a mixture Model that is not Gaussian

I am attempting to fit with Python a histogram which presents a bimodal distribution. For now, I have tested to use the Gaussian Mixture Model (GMM) from Scikit-Learn, but I want to try with different ...
Jack's user avatar
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Generate new sample with higher variance, covariance from empirical distribution

I am analyzing the joint distribution of three continuous variables. I would like to sample from the joint distribution of these three variables, which is straight forward. However, I also want to ...
siebenkaese's user avatar
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Python Library Trend time series multivariate

Our csv contains 36 columns 1 date time column collected every 30 mins 3 variables (count,latency,Totaltime) x 10 Features(user io, serverio ,concurrency ..etc ) Of different data points from the ...
trent's user avatar
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Critique my algorithm for measuring similar/difference of groups using multiple variables

So I've been trying to solve a problem of quantitatively measuring the similarity/difference between groups in my dataset. I am not trying to cluster data to create groups, because the groups are ...
pubb's user avatar
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Creating a dataframe using roll-forward window on multivariate time series

Based on the simplifed sample dataframe ...
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How to calculate the KL divergence for two multivariate pandas dataframes

I am training a Gaussian-Process model iteratively. In each iteration, a new sample is added to the training dataset (Pandas DataFrame), and the model is re-trained and evaluated. Each row of the ...
guest001's user avatar
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Why does the PCA Scores plot (PC1 vs PC2) flips when using extracted variables from the Gaussian?

Imagine you have some multivariate data (1000s of variables) which approximately follows the Gaussian distribution. You can generate various PCA Scores plots from this data, of course. One option is ...
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Predicting children growth

I am doing a project where I am supposed to forecast future athletes' performance one, two, three, etc. years in the future. The dataset consists of athletes' scores on tests done from they were kids ...
Erik Vabø Vatsvåg's user avatar
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Using scipy.minimize to find the maximum likelihood estimates for multivariate gaussian

Let's say I have a 100x2 normally distributed array of data. ...
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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 ...
chronosynclastic's user avatar
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Which dataset for multivariate time series forecasting

I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ? Can I use a dataset ...
Djakarta_zero's user avatar
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regarding computing the centroid of high dimensional data

In scikit-learn, or other python libraries, are there any existing implementations to compute centroid for high dimensional data sets?
user297850's user avatar
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Multivariate data preprocessing

I am trying to understand how multivariate data preprocessing works but there are some questions in my mind. For example, I can do data smoothing, transformation (box-cox, differentiation), noise ...
Canovich's user avatar
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Meaning of the covariance matrix?

I wonder about the excessive usage of the covariance matrix across all kinds of machine learning tools. So far, for me, the covariance is just a pre-step to get to the correlation. And as there is an ...
Ben's user avatar
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MLE for Poisson conditioned on multivariate Gaussian?

I am writing some Python code to fit 2D Gaussians to fluorescent emitters on a dark background to determine the subpixel-resolution (x, y) position of the fluorescent emitter. The crude, pixel-...
olympiader's user avatar
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How to build multiple variable regression having a mix of numerical & categorical features?

There is a need to estimate Annual Average Daily Traffic Volume (AADT). We have bunch of data about vehicles' speeds during several years. It is noticed that AADT depends on the average number of such ...
Артём Ощепков's user avatar
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Linear transformation from one sample to another

Generate a Sample $\underline{Z_1}$ $\underline{Z_2}\dots \underline{Z_{5000}}$ , while $\underline{Z_i} \sim N_2[(0,0)^T,I_2]$ generate new sample with size of $ n = 5000$ by applying linear ...
Mahajna's user avatar
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Does standardization result in normal distribution?

I have a question about standardization (subtract mean, divide by standard deviation) of data consisting of different features with different ranges. I read some information that seemed to be ...
scarlett rouge's user avatar
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Getting mean and covariance matrix for multivariate normal from keras model

I have a dataset that has 6 input features and 5 output features. I want to use a keras sequential model to estimate the mean vector and covariance matrix from any row of input features assuming the ...
Tanzin Farhat's user avatar
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Should I concat multiple stock timeseries datasets into one?

I have several timeseries datasets of stock data, with fundamental indicators. I would like to build a model that selects stocks for buy and hold. I understand that to perform this task I have two ...
Ubler's user avatar
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Getting vague results using VAR time series forecasting in python!

Firstly, I am a beginner in this field of Data Science and have tried to implement some time series models for wind speed forecasting. Also, I am aware of the fact that some regression models might ...
The P Guy's user avatar
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Sequential sampling from Gaussian conditional not working

I'm trying to sequentially sample from a Gaussian Process prior. The problem is that the samples eventually converge to zero or diverge to infinity. I'm using the basic conditionals described e.g. ...
Jacob Holm's user avatar
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Getting a balanced sample across many variables

Let’s say each element in my population has several attributes. Let’s call then A, B, C, D, E, F. Let’s say, for simplicity, each attribute has 10 values (but could be any number between 2 and 30). ...
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Consolidating multivariate time-series information from many data sets

I am having trouble setting up a problem with regards to time series analysis. I have 30 data sets, where each set corresponds to a certain project. Each project has 7 features, and each feature has ...
Domenic Prestia's user avatar
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Does Anomaly Detection Algorithm works when the features are not correlated?

I am working on an Anomaly Detection Problem and the algorithm I used is an Autoencoder Multivariate Gaussian. The problem with my data is that it is unlabeled and not correlated. For example, let's ...
user3219871's user avatar
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Can GLM( generalized linear method) handle the collinearity between the predictor variables in a regression-analysis?

I'm a beginner in Machine learning and I've studied that collinearity among the predictor variables of a model is a huge problem since it can lead to unpredictable model behaviour and a large error. ...
Bharathi's user avatar
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How to draw a sample from data set with respect to a given categorical or numerical variable based on given freely chosen distribution? (Python)

Say I have a data set for some past period. Now new data appears and for a given variable in the data and we find that the distributions have shifted (for example with "age" it would be that suddenly ...
Jaan Olev's user avatar
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How can I fix regression model interpretation of feature?

I'm building a regression model to predict the values of a feature $Y$ given a set of other features $X_{1}, X_{2}, X_{3}..X_{n}$. Onde of these other features, let's say $X_1$, is known to be ...
Tiago Bachiega de Almeida's user avatar
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Hidden Markov Model with Autoregressive emission model?

So far, all standard HMM implementations I've seen assume some variation of a Gaussian Mixture (GMM) as their emission model. It can of course only have a single mixture component which reduces it to ...
user3641187's user avatar
2 votes
1 answer
199 views

Can the dependency between variables be deduced from data? And if so, how?

I have a data set $X$ that consists of $m$ vectors $\vec{x}$ of $n$ real-valued components. Each vector component lies within a corresponding predefined interval of valid values, which is the same for ...
coliva's user avatar
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LSTM features classification output

I am very new at this, so I might be wrong about my choice of model, but my problem is the following. I am trying to generate music, hence the reason I am using an LSTM. I have the following sequence ...
Marianne A.K's user avatar
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3 answers
850 views

How to interpret Correlation along with Coefficients of multiple linear regression ?£

I have 10000 samples. There are 4 independent variables and 1 dependent variable. The independent variables are all centered with 0 mean. I found the correlation coefficients between each of these ...
Selvam's user avatar
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How to resample one dataset to conform to the distribution of another dataset?

I have two datasets with 20 features, but with different feature distributions (DS_A and DS_B). How can I sample the DS_A to make its distribution similar to DS_B, with respect to multiple features?? ...
cybergeek654's user avatar
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Are there any methods to detect whole multivariate time-series as anomalous from a set of multivariate time-series?

Consider a scenario with Dataset D as {T1, T2, ..., Tn} and Ti is a multivariate time-series of length mi as {X1, X2, ..., Xmi}. Here each record of the time-series Xi is a vector of attribute values {...
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How to do Multivariate Adaptive Regression Splines feature selection in python? Specifically, I need the python equivalent of the earth function in R

This is the code in R: marsModel <- earth(eval(parse(text=paste(ResponseVariable,"~."))), data = data) #build model ev <- evimp (marsModel) Response ...
Anuj's user avatar
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3 answers
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Confidence intervals in multivariate linear regression

I am fitting my data to a multivariate linear regression $Y = BX + \Xi$, where the response is bivariate $Y\in R^{n\times 2}$, and the predictor is uni-variate but elevated to the projective plane to ...
Jsevillamol's user avatar
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Select the right distribution

I have a dataset like: ...
Nathalie's user avatar
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Sampling trying to keep as much multivariate variance as possible

I was thinking if anyone considered a sampling technique that would try to aim keeping as much of the variance as possible (e.g. as many unique values, or very widely distributed continuous variables)....
PascalVKooten's user avatar