Questions tagged [synthetic-data]

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Generate a syntetic log-normal two dimensional random field

I would like to test some functions that I wrote related to the kriging applied to rain data. In order to do that, I would like to generate a synthetic log-normal 2D random field. The idea is to ...
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FID as a metric to evaluate the quality of synthetic datasets (Non GAN generated) for training models for a given classification task

I am working on a problem of generating synthetic data (algorithmically by blender, not using GANs) to aid the training of some CNN for a classification ask. Ideally, I want to generate an algorithm ...
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How to synthesis data with one non/faintly correlated feature with dv, but important feature in a regression?

How to synthesise artificial data with two features and a dependent variable such that one feature is faintly correlated with the dependent variable but becomes significant in linear regression?
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Tried so many different models but cant get good accuracy

I am very new to the field of Machine Learning. My college seniors provided me a dataset to analyze and predict. The data is purely synthetic, with 14 feature columns and a target column having values ...
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1 answer
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How to create synthetic data for this case?

Some weeks ago, I ran an experiment with 30 participants. For each participant two microphones were recording them while they were reading a phrase in 3 different simulated emotions (Happy, Neutral ...
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Simulating Hierarchical Data

I want to simulate a dataset that has a "grand mean", and then group means (with some deviation from the grand mean). Nevertheless, I have a bit of a problem conceptualising the problem: if ...
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Metric to assess similarity of distributions

I am working on clinical synthetic data and I would like to learn more about metrics to compare synthetic vs measurements distributions. As there are methods to generate synthetic distributions with ...
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1 answer
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Synthetic multivariate time series for anomaly detection

I built an anomaly detection classifier which worked perfectly with the anomaly detection task in my dataset (multivariate time series). Now I'm trying to understand what are its weakness and my idea ...
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3 votes
1 answer
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Synthetic data set generation for binary classification based on paper (interpretation problem)

I'm reading a research paper about fraud detection (unbalanced binary classification) where the authors go for synthetic data for evaluating their methods. I want to reproduce their synthetic data but ...
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Why is the standard error in a diff-in-diff with 4 datapoints 'not well defined'?

I was looking at this online book https://matheusfacure.github.io/python-causality-handbook/15-Synthetic-Control.html in order to learn about synthetic control. The author gets two cities in a panel ...
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Synthetic to real image translation

I want to train an object detection model with synthetic data, After testing it on real data, but the model developed based on synthetic data may not be adapted for real data. Therefore, I want to use ...
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Generating data for one-class dataset

In case I have a dataset that have only one class unlabeled (benign), could you please list some algorithms/papers that are used to generate complementary data (malignant) based on benign data only? I ...
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Composite Indicator - Negative Correlation Issue

I'm trying to build a composite indicator that aims to measure poverty. I'm planning to aggregate a series of variables using PCA. However I have a doubt, all the variables I want to include are ...
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Generate data that matches a frequency distribution while preserving the original spatial structure

I am dealing with a 3D array containing values representing the "importance" of each voxel. For my analysis, I would like to synthesize n new arrays from my original array to have a ...
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Is creating artificial class imbalance in synthetic training data a good way to tackle hard cases in classification?

I have a problem where I need to classify something around 50 different classes. Some of the classes are very similar to one another and the algorithm tends to confuse them. However, I can create a ...
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Learning Distribution of Data [duplicate]

Sometimes it's important to generate data due to data imbalance issues. I heard that data augmentation by leaning distribution of data is a hot topic now. Could you please give me some resources and ...
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2 answers
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How to generate synthetic data from a balanced dataset?

Let say I have a balanced dataset that has a small training sample size (lack of data). How do I increase the training sample size by generating synthetic data based on the original data? I believe ...
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What can be a good way to generate data(tabular) from statistical facts and probability of data?

For Example, If I have facts saying that: 50% of humans are male 30% of males are Indians 70% of Indians are brown average age of Indians are 27 30% of indian females are working 80% of no-indian ...
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1 answer
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Simulated data for statistical framework testing

I want to generate two sets of simulated data (numeric) for statistical testing. Is it possible to generate datasets with predefined RMSE or accuracy?
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2 votes
1 answer
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Generating samples on an exponential distribution

I am trying to generate a synthetic earthquake database where the number of events ($N$) with magnitude ($M$) in the range $[M, M+\delta_M]$ follows: $\log_{10}(N) = a - bM$ where $a$ and $b$ are ...
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3 votes
1 answer
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Does it make sense to use the KL-divergence between joint distributions of synthetic and real data, as a evaluation metric?

The KL-divergence is defined as: $D_{KL}(p(x_1)∥q(x_1))=\sum p(x_1)\, \log \Big( \dfrac{p(x_1)}{q(x_1)} \Big)$ I consider the Kullback-Leibler (KL) divergence as a performance metric for data ...
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Constrains on the coefficients of SARIMA

I am trying to generate synthetic time-series through SARIMA random process by defining the model coefficients manually. Could any one help me how to generate coefficients? What are the constraints on ...
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Generate more data for a small dataset

I have been working on a dataset which has 14 attributes and 303 rows(instances) along with the binary labels. I want to generate more data so that I could train my neural networks so that I could ...
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1 vote
0 answers
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Regression on unevenly distributed high dimensional dataset

I have a very high dimensional (20K+ hand engineered features) biological dataset to predict a single continuous output variable (such as a mental state exam scores for a dementia patient). The output ...
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0 votes
1 answer
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how to synthesize a virtual control arm from multiple historical trials with different treatment in their control arms

We want to synthesize a virtual control arm for a single arm study, and we found two trials with data available that targeting the same population. One trial used Placebo+Standard of care in the ...
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23 votes
10 answers
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Best term for made-up data?

I'm writing an example and have made up some data. I want it to be clear to the reader this is not real data, but I also don't want to give the impression of malice, since it just serves as an ...
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1 answer
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Generation of synthetic data for Hierarchical clustering

I wanted to test various hierarchical clustering algorithms to check which algorithm performs best. For this, I was considering simulating some ground truth. Is the possible to generate a correlation ...
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1 answer
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Combining treatment units for synthetic control method

Cross-posting this from the Stata forum to increase visibility - but let me know if that's an issue! I am thinking about using the synthetic control method to evaluate an intervention that took place ...
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Using Synthetic Data Based on Real Data for Classification

The goal is to classify three different cell types based on certain features (e.g. area, shape tensor etc.). However the amount of labelled training data I have is very small. Therefore, it was ...
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1 answer
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Synthetic Control Method with Correlated Variables

I am interested in using the Synthetic Control Method to measure the effects of a certain policy. I am afraid that the synthetic control will not be accurate because the underlying variables that make ...
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0 answers
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Creating synthetic data from existing data

I have data with 8 features and 900 rows each. Those features are dependent on each other in some way. I want to conduct machine learning, but the data size is small. I was suggested to generate ...
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2 votes
0 answers
207 views

Generating Correlated Random Variables following Weibull Distribution

I am working on analyzing power generation from geographically distributed wind farms. I have wind speed data that have been collected at multiple locations, but the measurement periods for different ...
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0 votes
1 answer
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Sample whole number from distribution with average less than 1

I am trying to create some simulated data, where for each participant, the average number of events that occur in a given week can be any positive number, but usually in the range of 0 to 5. Trouble ...
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Handling imbalanced data for classification [duplicate]

What are the best ways to deal with imbalanced datasets for classifying whether or not individuals pay their tuition? The data is 75% positive class (paid) and 25% negative (unpaid). Some approaches I ...
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Data Synthesis based on Deep Learning

Is there any open source tool available to synthetically generate a new dataset with the same statistics than the original one? The objective is to create a new tabular dataset that is private but ...
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1 vote
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"Add White Gaussian Noise with SNR" vs. "Add 5% Gaussian Noise"

I have a noise-free dataset which is a vector of numbers, $\mathbf{d}$, with length $N$. I want to "add noise" to this data. My understanding is that there are two ways to do this. 1) Add some ...
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12 votes
1 answer
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Creating an Imbalanced Dataset

I would like to have my trained model tested on an imbalanced dataset. Is there any algorithms available to generate synthetic data from a balanced labelled dataset (spam/non-spam)?
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1 vote
0 answers
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Methods for generating regression data whose target follows an arbitrary distribution

I'd like to generate a regression dataset (independent vars that combine somehow into a target or dependent variable). Generating such a dataset is, in general, easy: we can e.g. pick some $x_i$ at ...
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3 votes
0 answers
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Is it possible to use a time series to construct a synthetic control variable?

Imagine I want to reconstruct a counterfactual euro-dollar exchange rate in 2010 using a synthetic control variable to assess the impact of some policy. Could I use, for example, the exchange rates ...
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-1 votes
1 answer
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Generalizing "Causal Impact" synthetic controls, to multiple outcomes

Does anybody know a way to generalize the use of the Causal Impact google R package to multiple outcome time series? Say I ran a time series experiment and was able to set up multiple test outcome ...
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1 answer
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Generating synthetic data for robust regression?

I was running some test to see the benefits of using robust regression over ols. For that purposes, I created synthetic data in the following manner, A is the ...
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0 votes
2 answers
180 views

Generating a synthetic dataset from a latent class model

In latent class analysis, the estimated model paramters are class sizes and item-response probablities in each class. Based on these basic parameters, is it possible to generate a synthetic sample ...
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How to evaluate whether the mean of the synthetic time series is statistically equal to the historical mean

I am testing a stochastic model that generates synthetic hydrologic time series (five-year series, 60(month) data points). I want to evaluate whether these synthetic time series have the same ...
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1 vote
0 answers
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Using fake datasets to optimize a model

I have a machine learning application I'm working on that will make predictions based on data about retail stores. Problem is, any data collected by BI regarding the performance of a given retail ...
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2 votes
1 answer
34 views

Generating a matrix so that correlations amongst columns are close to pre-defined values

I want to generate a matrix of values such that the correlations (e.g., Pearsons correlations) are close to a pre-defined set of values (e.g., ...
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0 votes
1 answer
163 views

Over-fitting because of Artificial Synthesis of Data

In the process of artificial data synthesis in Machine Learning applications, when we introduce noise to generate new data, is it possible that overfitting can occur because we are essentially ...
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5 votes
0 answers
160 views

Is it possible to determine if a dataset is real or randomly generated?

I've been tasked with developing regression and classification models for time series data. For each observation I have a continuous target for regression and a discrete target for classification. I'...
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1 vote
1 answer
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When to use synthetic data and when to use regularization parameters to avoid the over fitting and which is better?

Can anyone explain me when to consider generating the synthetic data or when to consider regularization parameters to reduce the error so the machine learning model will not overfit
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1 answer
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Synthetic Control when leaving treatment

I'm trying to do a comparative analysis using synthetic control to compare a country that went from a dictatorship to democracy to it's dictatorial synthetic twin. As I understand it, synthetic ...
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Validating decomposition of Synthetic Tensor generated from Unevenly Sampled Tensor

I have a 3-way tensor generated from 7 experiments, with each experiment being matricized and becoming a frontal slice of the tensor (thus mode-3 is of length 7). The data is generated from ...
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