Questions tagged [forecast-combination]
The process of combining different forecasts to get a better resulting forecast than any of the constituents. Simple forecast averages are often found to outperform individual forecasts in practice.
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Accuracy for forecasted value in R
I forecasted a combined model with using full data set. Problem is when I use test data set my accuracy coding works fine but when I use the same for my combined data set with full data set it does ...
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How can I combine multiple regression models? [closed]
I'm trying to predict some financial feature (continuous) and there are two or more good regression models. Is it possible to combine multiple regression models? If so, what kind of method is it ...
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Can regression forecasts of univariate time series be independent (of one another)
Suppose I have short-term forecasts from two univarite regression models of the same time series. I am choosing the models to be as different as possible in structure and assumptions. For instance, ...
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Foreacast Combinations: derivation of minimum MSE / variance approach
I am just despairing of the derivation of the minimum variance procedure. The method of the combination of forecasts was first established in 1969 by Bates and Granger. They also invented the minimum ...
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Compare & Combine multiple models
We want to make predictions on customers who are suspected of money laundering. We train boosting models of male and female respectively. (There are 70% of male customers every day.)
We will select ...
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POSSIBLE COMBINATIONS AND DELETING THE UNWANTED
In our company we're capturing scenes from the cars cameras. The customer will say how many scenes they want to be let's say sunny, how many rainy etc.. Let's say we have 100 scenes to be captured. 80%...
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How to combine observational and experimental data?
I’m trying to figure out the effects of system changes on user long-term revenue (over a 12-month period, say) for an online platform.
I have a lot of observational data, so I fitted a model that ...
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Multivariate Time series forecasting- Statistical methods
I was trying to forecast the truck numbers required at each distribution location...for that I was forecasting the shipments(number of units) at each location and dividing it by a factor to get the ...
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Regression/Optimization models that factor in the conviction of the predictive values
This comes up in a problem I meet in practice.
Consider the classical regression of combining two predictions together to form a stronger prediction $Y
\sim X_1 + X_2$. Here essentially we generate ...
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Bayesian Model Averaging (Bayesian Averaging of Classical Estimates) Issue
I am trying to implement a method used by this paper (Described briefly at the bottom of page 3):
From what I understand, it does 2$^k$ OLS regressions each time step to forecast the next time step ...
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Combining non-probabilistic models for higher quality predictions
Three people have independently developed models for predicting a coin flip. They take into account the launch angle, launch force, rate of spin, and various other factors to produce predictive models ...
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Combination of different estimates of the same quantities?
Suppose we have $n$ number of estimates for a parameter, each derived independently. How will one combine these estimates to get a single estimate which has lower variance than each individual ...
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Time Series Forecast for a time series getting updated
I am working on a forecasting problem, where i am planning to forecast the value for the current time step (real value 43 in data below in a[4] column). The data is in the form of values at each ...
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How can I calculate Conditional expectation using copula
Let X, Y two time series and $F_{i, \beta_i}$ the marginal distribution of residual of each time series and beta is vector of their parameter. I studied the dependence between this two series using ...
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Lasso for Ensemble Learning, base learner selection
In ensemble learning, we average the predictions of multiple base learners (e.g. SVM + ANN + Linear regression). Instead of taking the mean of the individual base models' predictions, can lasso be ...
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Forecast combination using optimal weights
I am struggling with a case where I am supposed to calculate optimal forecast weights of two forecasts. We have fitted the models on a training set (time series) and want to calculate optimal weights ...
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Forecast package in R
I have one question which is maybe very simple.
So my question is does models from forecast package in R (e.g auto.arima,ets,tbats,nnetar etc) are machine learning models or not?
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Combination of correlations: How to correlate compositions?
How to correlate a set of compositions to a same-sized set of estimates of these compositions?
-> composition(estimated) vs composition(real)
Imagine you have a mixture of 5 liquids A+B+C+D+E, ...
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Averaging individual predictions in a group
I created linear model to give prediction for a team member (individual). Can I use this model to give average (individual) prediction in a team by providing average values of features among team ...
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Forecasting time point not value
I have a simple question.
when we want to forecast a time series, we always focus on the value of series in future. But could we forecast time point of spesific value? For example I would like to ...
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Combining regression models from separate data sets
What is the best way to combine regression betas from separate data sets?
For example, a data set is split in two based on some fundamental characteristic, and the same two factor regression is run ...
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Weighting of multiple linear regressions in an ensemble
If I have a continuous dependent variable and N continuous predictors, and I fit all possible regressions with zero up to N variables, how should I weight those regressions for prediction? One ...
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Combine mutliple predictions
This question had been asked several times in here, but I think I have something new to add.
I'm interested in predicting if some specific event will happen (binary classification). I have two ...
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Combining forecasts or distributions to form a more accurate one
Suppose you are interested in getting as good an estimate as possible for a random variable $X$, this could be for example a stock price in the future. You go to see $N$ experts, each gives you a ...
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Combination of hierarchial time series forecasts with different methods - setting weights
I am trying to forecast the the number of orders for different products of a product group. I have the time series for each product.
One of the problems is that some/most time series are intermittent ...
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What are the algorithms for fusing time series together [closed]
Assume I have multiple time series with the same length and the same range. What are the different algorithm used to fuse the time series together? what techniques would the best to combine them to ...
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time series forecasting in R for a period less than 2 years(18 months) which is totally random
I'm working on a project of forecasting. I have the count of the purchase order for an 18 months period of time.
I'm attempting to create a forecast from time series data that has observations only on ...
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A Function to select a forecast method
I often have more than one time series to fit a model. Thanks to forecast and forecastHybrid packages they make easy to fit a ...
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How to weight several noisy estimates of the same value
If you have a variety of noisy estimates/measurements of a single value, what is the best way to combine them in order to estimate the underlying value? I have looked at "Unknown Constant in Additive ...
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Using information about covariance between ARIMA models in forecasting
I'm interested in how to incorporate information about the covariances of related timeseries from multiple univariate forecasts into each forecast. The ultimate goal of this is to implement ...
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Price change in forecast
I recently joined a online retail company and the way they have been doing forecasting and inventory management is not good at all and I'm working on improving the forecasting of the products. While ...
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Simulation based on BSTS model
Currently I'm fitting different time series models and produce combined n-step-ahead forecasts. As finding prediction intervals (analytically) for combined forecasts is quite a hassle, I decided to ...
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Combine and reconcile forecasts
I am struggling conceptually with how I can best model my panel dataset. I have a set of individual data and I need to:
1) estimate the median value of the dependent variable over time in the entire ...
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Finding relevant combinations of predictors
I am currently working on a data set consisting of 300 predictors and a dependent variable. The predictors are categorical variables (taking values 0,1) and for every observation only a subset of them ...
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Optimization of Mean Absolute Error with regularization
i have two different weather forecasting systems. Each system returns values between 0 and 30 degrees. In addition i have a grounded truth set containing the real temperature values. Now i want to ...
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Approaches to average forecast in machine learning?
I have researched some approaches so far. My situation is:
I have 9 different models, all targeting on the same time-series variable.
Now I want to combine these 9 forecasts to estimate a better, ...
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Arguments against model or forecast combination?
Do you know any references providing arguments against model or forecast (models output) combination?
Could not find anything
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Do model averaging and model combination mean the same?
I am not sure, but I guess model averaging and model combination and even forecast averaging and forecast combination are used arbitrarily in the literature...
Is this only my feeling or indeed the ...
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Time series forecasting with a combination of methods
So,
i read this article: https://www.r-bloggers.com/timeseries-forecasting-using-extreme-gradient-boosting/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%...
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Model averaging approach -- averaging coefficient estimates vs. model predictions?
I have a basic question regarding approaches to model averaging using IT criteria to weight models within a candidate set.
Most sources that I have read on model averaging advocate averaging the ...
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Weights to combine different models
I have built different classification models (logistic regression, randomforest, and xgboost) for a dataset. I would like to combine the prediction of all the models to reduce the variance and ...
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Caret package - Is it possible to compute predictions for non-optimal models?
Not sure if this post belongs here or if stack overflow would be more appropriate.
I am starting to familiarize with the caret package in R which seems very powerful for the purpose of optimizing and ...
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How to implement Bayesian Model Combination?
I'm interested in formal procedure mentioned in "Turning Bayesian Model Averaging Into Bayesian Model Combination" (Kristine Monteith 2011). I have a set of $N$ "best" AIC ranked models and I want to ...
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Combining Forecasts: Best Information to Solicit from Forecasters?
Suppose
Statistician $m=1$ produces a set of $h$-step-ahead point forecasts $\hat{x}_{t+h|t, 1}$ of $x_{t+h}$ where $x_{t+h} \in [0,1]$.
Also, this point forecast could come with:
a predictive ...
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Dangers of averaging between model approaches
I am working with some ridership data that is broken down by route, year and month. I have built and tested a whole bunch of models ranging from GLM, GEE, GENLIM, and Panel and ARIMA data models. I ...
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Assigning Weights to An Averaged Forecast
So I've been learning how to forecast over this summer and I've been using Rob Hyndman's book Forecasting: principles and practice. I've been using R, but my questions aren't about code. For the ...
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How does Weka combine the decision trees in a random forest?
When building the random forest, I am wondering if Weka combine the decision trees by averaging their probabilistic prediction or if Weka let each decision tree vote for a unique class?
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Combining probabilities/information from different sources
Lets say I have three independent sources and each of them make predictions for the weather tomorrow. The first one says that the probability of rain tomorrow is 0, then the second one says that the ...
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More Statistical Way to Average N Predictions
I've run a RandomForestRegressor (Scikit Ensemble) over N loops, each time changing the random seed and therefore changing the train test split. This way I've N sets of predictions (M predictions for ...
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Model averaging in prediction -- "Wisdom of the Crowd"
Suppose I'm trying to predict $Y$ (a real number) and I have $n$ experts with guesses $Y_1,...Y_n$. Each prediction is a reasonable guess as to the value of Y in itself (hence the name "expert"), but ...
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