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Questions tagged [multivariate-distribution]

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How to analyze time series data and create time series model in Python?

I am trying to understand time-series data and model. In youtube tutorial and others, mostly univariate examples are shown. And they are applicable or suitable for those conditions. What if our ...
Bad Coder's user avatar
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0 answers
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How to organize multi-layer data in Orange Data Mining?

I have data in the form of a MATLAB cell array in which: Rows are individual ROIs columns are image channels But each element of each column stores not only the mean intensity value of the ROI, but ...
DopeOmics's user avatar
3 votes
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How to predict multi-variate time-series from different samples [closed]

I'm having issues seeing the best way to predict a time-series when training on a dataset with different samples. I have a dataset that shows the weight of 10 rabbits from their first day to their ...
scootjow's user avatar
1 vote
1 answer
158 views

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 ...
rpiston's user avatar
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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 ...
aa aa's user avatar
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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 ...
Laurimann's user avatar
1 vote
1 answer
71 views

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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1 vote
1 answer
537 views

Creating a dataframe using roll-forward window on multivariate time series

Based on the simplifed sample dataframe ...
user1934212's user avatar
1 vote
0 answers
190 views

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
0 votes
1 answer
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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 ...
Jimonty's user avatar
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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
1 vote
1 answer
2k views

Using scipy.minimize to find the maximum likelihood estimates for multivariate gaussian

Let's say I have a 100x2 normally distributed array of data. ...
Bazoya's user avatar
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1 vote
0 answers
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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
0 votes
1 answer
308 views

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
1 vote
1 answer
305 views

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
0 votes
1 answer
98 views

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
1 vote
1 answer
261 views

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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0 answers
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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
1 vote
1 answer
43 views

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
1 vote
1 answer
55 views

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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1 answer
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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
0 votes
1 answer
518 views

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
0 votes
2 answers
142 views

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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1 vote
0 answers
235 views

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
1 vote
0 answers
24 views

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
0 votes
1 answer
218 views

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). ...
user's user avatar
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0 votes
2 answers
57 views

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
-1 votes
1 answer
43 views

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
2 votes
2 answers
2k views

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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1 vote
1 answer
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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
1 vote
2 answers
97 views

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
1 vote
0 answers
52 views

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
226 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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1 vote
0 answers
38 views

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
2 votes
3 answers
1k 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
  • 93
2 votes
1 answer
1k views

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
1 vote
0 answers
27 views

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 {...
Sushodhan's user avatar
  • 131
2 votes
0 answers
130 views

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
  • 21
2 votes
3 answers
315 views

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
1 vote
0 answers
49 views

Select the right distribution

I have a dataset like: ...
Nathalie's user avatar
  • 147
1 vote
1 answer
101 views

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