Prediction is concerned with assessing the probability of unknown values from known values and inferred relationships.

learn more… | top users | synonyms

1
vote
0answers
8 views

Should I use 'sample standard deviation' for 'prediction intervals' and 'standard error of the mean' for 'confidence intervals'?

I just want to make sure I am getting this right. When I am concerned about the "result" of any particular new datapoint I should use prediction intervals not confidence intervals. When I am concerned ...
0
votes
0answers
6 views

LSSVM Prediction using LS-SVMLab toolbox v1.8

I'm trying to forecast a time series of air passengers using LSSVM with the help of the LS-SVMLab toolbox v1.8 from http://www.esat.kuleuven.be/sista/lssvmlab/, specifically the NARX model function. ...
0
votes
0answers
14 views

Combining continuous spatial and discrete time series methods for spatial prediction

Here's something I've been pondering. Wondering if anyone can shed come light on it/recommend some references/tell me why it makes no sense, please. In my field (predicting crime risk by location), ...
0
votes
0answers
16 views

Estimate battery's real capacity in real time

The maximum capacity of a battery during usage can vary from manufacturer's specifications due to factors like temperatures and battery health. Because of that i want to estimate the real maximum ...
0
votes
0answers
24 views

Machine learning for detecting bank transactions fraud [closed]

I have to develop, as much as I can, a bank transactions fraud detection system in under 5 months. Im using complex events processing, and as I see it I have to implement, firstly, a system that ...
0
votes
1answer
27 views

Definition of residuals versus prediction errors?

I always thought the definition of residuals is the difference between the statistic and the observations. And, the definition ...
0
votes
1answer
26 views

Machine Learning Procedure for Fractional/Proportional Data?

I am looking for some suggestions of machine learning procedures that work to predict fraction outcomes where the outcome variables $\in [0,1]$. Can you provide me with any suggestions? I thought ...
0
votes
1answer
35 views

Predicting values from linear regression

I've got a data set consisting of olympic years and the winning times for the womens 100m. I can plot a line throught the data using matlab as such: ...
0
votes
0answers
17 views

10-fold cross validation of a Multinomial Regression Model SPSS 20.0

I have a set of 125 people that belong to one of four nominal categories. Each person is described with 7 descriptors with 2-5 nominal variables that I use in my regression model to predict the ...
1
vote
1answer
30 views

What statistical method would I need to employ to predict a school's attendance if I know previous five years' attendances?

I have attendances month by month (September-June) for a given school X for the following school years:2011-2012, 2012-2013, 2013-2014, 2014-2015. I also have the attendance for students at school X ...
0
votes
1answer
56 views

Calculating the probability a predicted point is 0

I have a deterministic function $f(x)$ and have evaluated some points $x_1,...,x_n$. So essentially I have pairs of data $(x_1,f(x_1)),...,(x_n,f(x_n))$. I am modeling the function $f(x)$ using a ...
1
vote
0answers
15 views

Estimating the battery capacity using current power consumption and battery percentage

I want to estimate the current maximum capacity (in kWh) having the current power consumption (in kWh) and the state of charge of the battery (in %) available in a time series. I do not have a full ...
0
votes
0answers
21 views

Gaps of methods to evaluate prediction accuracy

There are many methods to evaluate prediction models based in prediction errors, such as MSE, MAE, MAPE, WMAE, etc. These methods are usually used in data prediction competitions, where one is given a ...
1
vote
0answers
20 views

Aggregating estimates for weekly data

I have a data set (in R) measuring the performance of all machines in a factory at the end of one week. Each machine has various metrics captured (feature vector) and the ppm defects (predicted ...
0
votes
1answer
23 views

Metrics to asses the ability of a model to predict a probability

Problem setting Here is the problem. A customer is faced to several products corresponding to the needs he has expressed. The goal is here to predict the probability of each product to be chosen. ...
1
vote
2answers
43 views

Best statistic for measuring prediction accuracy that is robust for outliers

I have recently built a model, designed for prediction. Initially, I chose model A over B - better RMSE and better MAPE. However, after carefully evaluating each prediction on my test dataset for ...
1
vote
0answers
11 views

Does cross-validation for model selection using MSPE make sense for mixed models?

Does cross-validation for model selection using mean square prediction error make sense for mixed effect models such as produced by lme4? If so, under what conditions / caveats? Also, are there any ...
1
vote
0answers
17 views

Proof for Irreducible Error statement in ISLR page 19

This section of Introduction to Statistical Learning in R (page 19 in v6, statement 2.3) is motivating the difference between reducible and irreducible error (that is noted by $\epsilon$ and has mean ...
0
votes
1answer
7 views

Evaluating predictions consistently

I am confused about how to assess predictions I make (specifically on PredictionBook, but I believe my confusion isn't specific to PredictionBook). I'm not sure if Cross Validated is the proper place ...
0
votes
0answers
15 views

Applying an uncertainty to a prediction

I am estimating/predicting the numbers of students who will pass on an exam at a school this year. My method is very simple: Each student give me a guess of their own grade one month before the test. ...
0
votes
0answers
13 views

Inference in Bayesian network Using bnlearn package

In this link Prediction of continuous variable using "bnlearn" package in R , the author talk about how I can find the conditionl probability of P(node(C)\ the rest node)=P(C\A,B,D,E,F,G) ...
0
votes
0answers
12 views

“Cooking ingredients” problem

I have a problem like this. Suppose I have 3 persons, A,B, and C to make cookies with 3 basics ingredients, flour, egg, and water. The recipe to make cookie is unknown but we need too determine the ...
0
votes
0answers
27 views

Investigating characteristics of good prediction schemes

Short: I am evaluating prediction schemes. Using which formalized (statistical) method can I investigate what are the characteristics that differentiate the best from the worst in terms of their ...
3
votes
2answers
88 views

Using x to predict y when they are highly correlated

I am trying to use an organism's length (x) to predict weight (y), but length and weight are highly correlated (r=0.95). Are there any pitfalls I should be aware of because estimates of the two ...
0
votes
0answers
28 views

Test error of probability “prediction” for little league soccer

Background: I have multiple sets of team's little league soccer scores made up only of whole numbers [0-12 inclusive], eg: ...
1
vote
1answer
18 views

Looking for an error measure like MAE that weights overprediction more than underprediction

I am trying to evaluate my prediction results using common error measures like the MAE, MSE or RMSE. For me it is much worse if the predicted value is higher than the true value. If it is less, it is ...
4
votes
1answer
62 views

Predicting running times for track meet places

Good morning all, I'm a track/running coach in Texas. That's my gentle way of saying that I don't have a statistical background, but I'm trying to learn what I can. Forgive me if I don't use the ...
0
votes
0answers
28 views

Cross validation with model selection (in-sample fit and out-of-sample prediction)

I currently have 3 models to predict Y from a linear combination of independent variables: Model 1: Y ~ A + B Model 2: Y ~ A + C Model 3: Y ~ A + D Now, I want to compare their in-sample fitting ...
2
votes
1answer
46 views

Decompose ridge regression bias error into model bias and estimation bias

How can I show that the in-sample bias error in Ridge regression can be decomposed into model bias plus estimation bias? I.e., if $Avg$ takes the average over all the input variables $x$ in the ...
1
vote
1answer
27 views

Prediction or forecast error

I understand the general idea of different time series model fittings, calculations, and model comparison. However, I am a little confused of understanding the forecast of a time series model. For ...
0
votes
1answer
54 views

R predict function and Model Object [closed]

I'm doing machine learning in R. I would like to know how we can create a model object that we can pass to "predict" function along with new data so that we obtain predicted values. To elaborate, I'm ...
0
votes
0answers
36 views

Daily prediction using ANN in R: {neuralnet} package

Relying on fitting ANN, I've currently trained my neural network and have been glad with the results on my test set by comparing Actual vs Predicted values. Now I ...
3
votes
2answers
50 views

What ML algorithm(s) should I learn in order to predict based on multiple features the likelihood of a binary event occurring in the next week?

What ML algorithms should I learn in order to predict the likelihood of an event occurring in a specified time period from the present forward based on multiple features in historical data I've ...
0
votes
1answer
18 views

Over sampling imbalance data in SVM

Basically I have a medium size data set (20,000 observations) with only 200 being in group 1, thus an imbalanced data set. My goal is to predict as much group 1 class as possible without sacrificing ...
0
votes
1answer
33 views

How do I model seasonal patterns for underprediction?

I want to predict sales in food-vending machines (to ultimately prevent food waste). I work with scikit learn. My current models are not too bad, but they show ...
0
votes
0answers
13 views

Using known and complete data to predict data part of which is know and the rest is unknown

It is a general problem. I have a training set(size > 1000). Each data point has 1000 features. What I want to do is to use these training data to complete a data point which has 600 known features ...
1
vote
0answers
29 views

How to predict where someone will go given a history of different locations that person visited?

Title says it all. I want to predict where a person will go at time=now if we know they have a history of x unique visits at n different places. I think some relevant variables would be what hour ...
0
votes
0answers
24 views

Dimensionality reduction and dynamic weighing of large set of correlated features

This is a question on what's the right hammer for my nail. Bare with me, I'm not very experienced with machine-learning techniques yet, but need some pointers in the right direction. I have a ...
1
vote
0answers
15 views

Using Population Statistics to Predict future Population Statistics: General or Specific Demographic Groups?

I apologize in advance if I don't use the right terminology. Problem to Solve: I want to predict the voter turnout behavior of Black, Male, Non-latinos for Springfield in the upcoming election. ...
0
votes
1answer
39 views

price prediction in r using time series

I am trying to make a prediction of imbalance prices in the elctricity market. My dataset consists of data for every 15 minutes (this is the time period in which a price is determined) during 11 ...
0
votes
1answer
26 views

crossvalidation for forecasting sales

My objective is to predict the sales 6 weeks in advance. I have data that from 01-Jan-2013 to 31-June-2015. I am supposed to predict the sales from 01-Aug-2015 to 17-Sept-2015 using machine learning. ...
0
votes
2answers
41 views

Prediction Accuracy - Another Measurement than MAPE

I have the question for my prediction model, which estimates used car prices. For example: Car: 20.000 km, real price: 32.000 Euro, prediction: 27.000 Euro --> MAPE: 15,6% , absolute difference : ...
0
votes
0answers
18 views

Missing data associated with group

I am interested in estimating parameters and predicting y using a standard regression model: $y = \beta_0 + x_1\beta_1 + x_2\beta_2$ The data that I have consists of a population of two groups A and ...
1
vote
0answers
20 views

Bootstrapping versus formula-based forecast intervals - wrong approach?

I've implemented a comparison between the performance of 80%-forecast intervals is in the forecast package - see 1st part of the code below providing a number of hits This number states, how many ...
1
vote
2answers
58 views

How to validate my glm model

probably the title is not very clear but here goes : a built a gml model on my train set with model=glm(y~x1+x2) and now i'm predicting the output on the test ...
0
votes
0answers
8 views

Predicting completion time based on cut-off distribution - PDF extrapolation

I have a series of tasks ($T_1, T_2, \ldots, T_{100}$) and each was given to many individuals. For each task, a limited time ($L_1, L_2, \ldots, L_{100}$) was allotted for completion. For each attempt ...
0
votes
1answer
46 views

Missing factors in basic linear regression

My model produces some results, but seems to drop several factors for some reason. This then leads (I believe) to the error I get when trying to run prediction with the model on the test data-set. ...
0
votes
0answers
27 views

Generating SARIMA data and using it to evaluate the accuracy of the `predict` function, but getting some weird plots

I have written the following code to generate 500 data points from a $SARIMA$ model, use $400$ as training data and then predict the following $100$, while estimating the model with AIC. It appeared ...
2
votes
0answers
32 views

Generating an $ARMA(1,1)$ model with `arima.sim` in R, receiving warnings and errors but questioning the flaw in my method for estimation

So I wanted to generate $500$ data points from an $ARMA(1,1)$ distribution in R, use the first $400$ as my training data and use the training data and the predict ...
0
votes
0answers
29 views

Modeling with zero-inflation models for prediction

Below is the summary of my dataset: ...