1. R

    How to use a pre-trained model to predict on new data

    I am trying to look for examples where a pre trained model on one kind of dataset can be used to predict a new kind dataset and I would like to explored methods that do not use deep learning. I understand that it is possible to use a pre-trained model as an input for a new model but I am not...
  2. S

    Logistic Regression, maybe?

    Greeting and many thanks in advance I have been advised to use Logistic Regression to solve the scenario described below. Can anyone confirm that Logistic Regression is the correct statistical methodology? Or provide suggestions for a better methodology? Scenario A test has 20 questions Each...
  3. M

    Repeated measures to predict binary outcome

    I would like to predict outcome of an event based on repeated measures. The problem is the following one: I have 100 patients with measures of a certain feature at different times, but all the patients do not have the same number of measures. The test data has been taken before knowing the...
  4. O

    Solar energy prediction

    Hi, I am prediction one-day-ahead solar energy output using 30 days historical data. The data sets are hourly, so the prediction is done hourly from sunrise to sunset. I have doe the prediction using sliding window technique, When I am predicting 01/06, I am using 30 days historical data (from...
  5. S

    developing and assessing a prediction Cox model using lasso

    I wonder if anyone can comment on if the following modelling strategy is valid please? I have a 200 patient survival data set (actually 2 data sets: 40 events and 160 events) and 100,000 ish candidate predictors. I want to build a prediction model so I plan to use LASSO and elastic net (with...
  6. J

    Prediction rankings

    I am trying to predict rankings for these players for any season ahead. For example, how could I predict the ranking for player 1 for season #34? ur=unranked for that season
  7. J

    Modeling and predicting pathology from multivariate clinical data

    Hello, I have a clinical data set that consists of 5 clinical measurements on thousands of tissue samples. Furthermore, each sample has a pathology diagnosis that is 1 of 5 possible diagnoses (all different types of tumors). I am interested in predicting which pathologic class future samples...
  8. S

    Probability of a subjective event based on historical subjective data . Forecasting

    Lately i developed an idea of understanding the behavior of kids in their childhood. I was wondering how kids are molded into different adults in no time. So i created a problem statement based on my ideology and hoped to solve using applications of mathematics. Coming from a non engineering and...
  9. D

    vibration signal_prediction

    Hello, I have a vibration data coming from a motor. i am using R program to do the predictive analysis. My vibration data is a white noise (mean and variance are constant). I need to use this vibration data to do the prediction. How do i use this signal for prediction? Do i need to do some...
  10. M

    Best fitting model for one gold standard value ?

    Hello, trying to solve the following statistical problem in jmp: 1. I got a gold standard y (for example: y = 100) 2. I got three data sets x1,x2,x3: (for example x1=92;94;99 / x2=92;94;99;101;103 / x3=92;94;99;101;103;107;100;99;100) please note: the data sets have similar data...
  11. E

    Predicting outcome with multiple groups

    Hi everyone, I'm currently working with 3 different groups (3 disease populations) and I would like to predict which of the three groups they fall into based on a measurement we did. I was thinking that it should be something like multinominal logistic regression but how can I then get a...
  12. E

    Forecasting/Future prediction assistance

    Hi guys, I've got a bit of advertising data here and I'd like to make a model out of it which can predict future events. So I have the amount of money spent, the number of billboards we've got and how many people we think have seen the billboard. We also have the number of walk ins we...
  13. M

    Which is the better prediction model?

    The aim is to predict the breakdown time of a machine as a percentage of scheduled hours for the next day. So my time series looks like this, Break_down_percentage = 7%, 8%, 10%, 6%, 12 % etc. There are 315 data points which can be used to test the different models. I used ets(), arima()...
  14. B

    Brier score calculation: 2 methods should yield same result

    I have a set of 234 predictions of tennis match outcomes and 5 different prediction models. I use the two different methods for calculating the Brier score described, for instance, here. The first method is: BS=\frac{1}N\sum_{t=1}^N(f_{t}-o_{t})^2 Where N is the number of forecasting...
  15. Y

    Prediction from predicted/residual values compare to standard error of the estimate

    Hello. I have to indicate how good prediction is, by looking at the actual, predicted, and residual values, compare to the standard error of the estimate. I understand that the smaller standard error of the estimate is more accurate, and is a better prediction. But when “Residual score...
  16. L

    MLB Stats Model

    Looks like someone created a statistical algorithm to predict and track itself over the MLB season: Anyone know what this guy is using to predict this stuff?
  17. N

    How to differentiate between a global and a local method?

    Hello, I am trying to write a report based on prediction models and my topic requires me to cover one global method(regression for example) and a local method( nearest neighbour for example). I do not quite understand the term global and local in this context. Some help to explain these terms...
  18. L

    NFL Week 6 regression predictions Atlanta 54% NY Jets 79% Pittsburgh 52% Minnesota 73% Buffalo 55% Detroit 84% Denver 75% Houston 55% Miami 52% Seattle 59% Green Bay 80% Baltimore 51% New England 59% Philadelphia 76%
  19. L

    Logistic Regression of NFL Data

    Week 5 picks from a logistic regression model for the NFL:
  20. Y

    Statistical/ML models when observations have different amounts of input

    Let's say we're predicting an employee's performance review score for the following year based on that emplyee's metrics from each previous year of his/her employment. We might have these training observations below. Note that "2014i" means "that employee's set of input values from 2014", which...