Questions tagged [prediction]

Prediction of unknown random quantities, using a statistical model.

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predicting who will buy what [on hold]

what machine learning algorithm would be most suitable to predict which customer will buy what and when they will buy it ( consider historical data is available). I have tried : predicted next ...
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18 views

predicting future orders based on past ordering data [on hold]

I'm a student and as my project problem statement is as follows: a company has provided ordering data ( SKU, quantity,date of order, customer name) of past 3 years in form of an excel file. we want ...
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Time series predictions look suspiciously good

I am working on a time series forecasting problem. For this, I am training a recurrent neural network in Keras (mostly following the guidelines from this blog post by Jason Brownlee). My problem ...
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2answers
45 views

How to predict continuous outcome in multivariate data? [on hold]

I have more than 100 variables and the outcome variable is continuous (percentage of remission). I was wondering what kind of model can I use for prediction of these types of response based on the ...
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329 views

What is acceptable in a prediction model accuracy? [duplicate]

We have developed a Predictive Model to show certain elements increasing and decreasing in certain locations of country. What is acceptable in a prediction model accuracy?
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What is the difference between machine learning approaches and Fourier series to fit a curve to data graph?

As I know machine learning(at least in some problems) tries to fit a curve to data graph. And I think Fourier transform tried to do it. But machine learning use a hypothesis curve with the formula ...
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25 views

How can I reduce the noise of prediction graph? [duplicate]

I am trying to use LSTM to predict a time series data as you can see in the following image, the predicted graphs is very noisy: The original data is looking like this: That I normalized it like ...
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How is the method of Eviews dynamic forecasting? [closed]

One [answer]1 says that dynamic forecast use forecasted value instead of actual value.Yes it is logical. But other answer says dynamic forecast use n step ahead.Example if you want to 10 days or 100 ...
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19 views

How can I compare the effectiveness of a regression model for different datasets

I have a multiple linear model that works on different datasets. suppose that the first dataset produces y in range of [1,100] and the second one in range of [1, ...
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What do you think is the best method for prediction for network structures

Open Ended Question Here. Suppose that you have 10000 samples for 100 binary explanatory variables. Approximately every explanatory variable has a fixed probability of being 1 (say 25%). The outcome ...
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13 views

(VAR/VECM) Difference between fitted value and predicted value using in-sample data

My understanding of the mechanism of generating fitted values of VAR or VECM is much like lm() (perhaps since VAR/VECM use linear regression to estimate coefficients?), where the data is just used to ...
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How to use Hidden Markov Model for predicting the last state in set of sequences? [duplicate]

I have a dataset consisting of a set of sequences as follows: ...
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1answer
40 views

Logistic regression: use of the term “prediction”

I would be grateful for any advice on this. I am currently working on an analysis where we are trying to identify what variables would be most useful in predicting a particular binary outcome. We used ...
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Modeling probability mass over time for prediction

Consider a discrete random variable $X$ with three possible realizations $x_1,x_2,x_3$. This variable is observed over time, with the number of observations per point in time varying. The top ...
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Big difference in random forest test subset error and other subsets

I'm using R "randomForest" package for predicting stock prices. I have more than 3000 observations and 90 columns. I have excluded my last 150 days from data set and divide the rest of my data to ...
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22 views

Prediction model with lagged target variable as input

Including a lagged version of the target variable as input naturally improves the accuracy. A disadvantage I observe is that almost all the weight (e.g. in linear regression) is put on that feature, ...
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Categorical response - Expected prediction error derivation

I'm stuck at the formula derivation from 2.21 to 2.22, and then 2.23. It is from the book The Elements of Statistical Learning. Any pointers or links would be much appreciated
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31 views

Control data selection for a prediction model involving repeat measures

I am building a model is applied every 24 hours at a fixed time to predict outcome X in the subsequent 24 hours, amongst a population of patients. The model will make use of variables collected in the ...
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61 views

Not sure how this GLMM provides a better fit than GLM as indicated by R-squares

I have been learning how to draw the model prediction on a scatter plot, and noticed a bit counter-intuitive result. I would greatly help if you could kindly explain how I am mistaken here. Let me ...
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1answer
43 views

Penalize predictions with larger prediction interval

Suppose I am building a model for regression problems. I am quite curious about the following questions: Are there relevant theories that can confirm/disprove the following intuition: we should ...
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17 views

Prediction intervals for Lasso predictions

I am trying to find confidence (prediction) intervals for predictions of new data by a Lasso model. I've fitted a Lasso model to training data using the cv.glmnet function (i.e. by cross-validation) ...
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26 views

How to get a non parametric one-dimensionnal regression of a function from several specimens obsevation

What I want to achieve I am trying to get a prediction for an unknown function from several measurements done on several speciemens (function is actually a measurement along a specimen), and a ...
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25 views

What is the variance of a Brownian Bridge with vertical “gaps”?

Suppose I have a simple Brownian bridge with $B(0)=0$ and $B(1)=0$. Further I know for some $t \in (0,1)$ and $y>0$ that $B(t) \notin (-y,y)$. As far as I understand, the expected value on the ...
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1answer
37 views

What does MA(q) model forecast? Future $X_t$ or future $\epsilon_t$

In time series, Moving Average model $MA(q)$ is defined by $$X_t = \mu + \epsilon_t + \theta_1 \epsilon_{t-1} + \theta_2\epsilon_{t-2} + ... + \theta_q \epsilon_q$$ where $\mu$ is the mean of the ...
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Machine Learning Algorithm for Predictions Outside of Feature Space

I have a dataset with only a few features (about 5 or 6) that I am interested in using to make predictions related to an outcome, and the context in which I am making the predictions is outside of my ...
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14 views

How to encode categorical variables in a video game predictive model

I'd like to make a model to predict the result of a match in a video game (win or loss). The game is 3 players against 3 players, and each player has a specific character with specific ...
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48 views

Do out-of-sample fitting methods solve the problem of over-fitting?

Suppose we have a regression model, and we want to fit this to training data, and then make predictions on test data. There is a well-known danger that out-of-sample predictions will be poor, due to "...
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Determining the variance of a linear regression prediction from only mean and standard error

I'm not sure there's a mathematically valid way to do what I want to do. Let's assume a simple bivariate regression $$y = \alpha + \beta x + \varepsilon$$ I have the following results for this ...
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1answer
13 views

How to deal with the categorical variables with few data for prediction

The image below shows how the rating for the heating quality will affect sale price.The data is about apartments and it's properties. E.g Rooms, GarageSize, BasementSize, etc. This visualization will ...
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2answers
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Mixed models: predict random intercept with partial data

I have a dataset with growth data for teens between 12 and 18 years old, where I want to predict throwing speed for all ages using a couple of other predictors. These predictors have been collected in ...
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7 views

Predict outputs of random forest based on subspace of features

I have a regression random forest model and each year a new dataset that I'm using to determine my yearly outputs based on that model. However I get the different features at different times of the ...
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1answer
35 views

Predicting n'th percentile [closed]

When we use prediction, we can only say levels. For example: We have 500 sample data for our walking range. And let's say 90 percentile is 16.0 km and 10th percentile is 0.78 km. Well, can only say ...
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1answer
54 views

How to explain the result of this polynomial equation?

Lets say that I predicted plant productivity (logy) by precipitation (logx1) and moisture content of soil (logx2). my original data gives me the best result only after taking logarithm on both side of ...
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1answer
20 views

Logit model with all significant coefficients, but low prediction capabilities

I'm facing this issue with a logit model with R. I got my coefficients significant (already removed the Linear dependent variabiles and the not significant variables), as: ...
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32 views

How to replicate the predict function from R in Excel given I have access to “summary” output from R

I have run a 3rd order polynomial regression in R and have run the "summary" function, but I need to be able to replicate the "predict" function in Excel. I have my current working code below. Thank ...
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How to update probability of logistic regression to take account new information? [duplicate]

I am at an admissions office at a college. We are trying to predict the probability that various admitted students will actually matriculate (come). We have a deadline of May 15 that the admitted ...
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1answer
19 views

When to use Normalized Root-Mean-Squared Error vs Spearman Correlation?

I am doing some Machine Learning experiments with Azure and the graphs that it gives me are measured in Spearman Correlation vs Iteration Number (part of the machine learning) However I was just in ...
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1answer
45 views

Can MAD (median absolute deviation) or MAE (mean absolute error) be used to calculate prediction intervals?

From my understanding, RMSE (root mean square error) estimated through cross-validation can be used to calculate the prediction interval of a mixed-effect linear model with gaussian error. In my case, ...
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23 views

Estimating probability density for forecasts

I've used a handful of algorithms for forecasting future values in a time series. But sometimes what I'm really interested in is not the predicted value, but the probability that some future will be ...
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17 views

Nested cross-validation for performance assessment and simple CV for model building?

I am not sure if I understood Dikran Marsupial in this thread correctly: Dikran Marsupial comment on Jan 22 2019 at 11:38: Dikran suggests that nested CV is only used for performance estimates of ...
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Averaging GLM's based on delta AIC < 2 and using the resulting coefficient estimates with their associated raster to build a predictive raster

I'm trying to construct a predictive spatial raster based on averaged GLM's Not the exact code but an example: mod1 <- glm(c~ x1 + x2 + x3, data=data) mod2 <- glm(c~ x1 + x2, data=data) mod3 &...
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1answer
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Somers' D for model validation

Somers' D as defined for example in "The predictive accuracy of credit ratings: Measurement and statistical inference" by Walter Orth is defined for the case when Y is predicted by X as $$ D_{XY} = \...
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1answer
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Using one-hot encoded features along with continuous-valued features?

The task I wanted to do is a prediction task where most of the features are continuous numbers and some of the features are one-hot encoded. I am training a neural network and I wondered that, is it ...
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Predication of MDN

How to get the prediction of Mixture Density Networks? In MDN one models the conditional density: $P(y^i |x^i) = \sum_{j=1}^{m} \alpha_j(x^i)\phi({y^i|x^j})$, so I guess one just sample from the ...
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21 views

Which variable to predict when using Gaussian Processes: $y_*$ or $f_*$?

We have the following model: $y_t=f(x_t)+\epsilon_t$, where $f$ follows a gaussian process, and $\epsilon$ a normal distribution. Which quantity should I predict, $y_*$ or $f(x_*)$? The difference, ...
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1answer
59 views

Missing data when actually predicting - Additional model legit?

This is a theoretical question, but I have already stumbled upon this issue a few times: My learning data are not complete, but I manage to handle the missing values. Now it's time for actually ...
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1answer
23 views

Estimating potential moves in a time series relative to an other

Say we have two time series which over time have a strong correlation, say above 0.8, say for example the price of oil and share price of an oil exploration and extraction company. Now say that we ...
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Cross Validation with Panel Data of Variable Length

I have a problem of wanting to predict $y_t$ from $x_t$ and some lags given a series of $\{(y_t, x_t)\}_{t=1}^T$. To build my prediction algorithm I was hoping to use cross validation techniques such ...
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How to validate PLS2 with R?

I have a PLS1 modell wich predicts single chemicals. Now I would like to run a PLS2 modell to predict two or more chemicals from a mixture. I programmed it in R and the modell runs, but i don´t find ...
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1answer
147 views

Fitted RF: difference between the probabilities in $votes and predict (type=“prob”)

Say we have a data frame df where diagnosis is the first column. There are only 2 possible values for ...