Skip to main content

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.

Filter by
Sorted by
Tagged with
0 votes
1 answer
25 views

Converting quarterly growth rate forecasts to yearly growth rate forecasts

I'm generating forecasts on a quarterly basis, focusing on metrics like the GDP growth rate for Brazil. These forecasts are presented as growth rates. For example, the forecast for 2020Q1 represents ...
Afiq Johari's user avatar
0 votes
0 answers
30 views

Autoregession meets multiple regression? - Help with verbiage and approach

Needing some help with verbiage and opinions on how I am approaching this model. I have counts of people over the past 24 months. month count 1 100 2 105 ... ... 24 200 First, I reverse the ...
Tyler Brown's user avatar
1 vote
1 answer
65 views

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 ...
070701's user avatar
  • 61
4 votes
1 answer
59 views

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, ...
andrewH's user avatar
  • 3,157
1 vote
0 answers
25 views

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 ...
witnes-real's user avatar
1 vote
0 answers
22 views

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 ...
yan zhuang's user avatar
0 votes
1 answer
40 views

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%...
MariaM's user avatar
  • 1
1 vote
0 answers
68 views

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 ...
Philip A's user avatar
0 votes
0 answers
84 views

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 ...
Arvind Menon's user avatar
1 vote
1 answer
45 views

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 ...
CuriousMind's user avatar
  • 2,263
2 votes
0 answers
102 views

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 ...
dafdaf's user avatar
  • 21
5 votes
2 answers
67 views

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 ...
WhiskeyHammer's user avatar
0 votes
0 answers
24 views

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 ...
Abhishek's user avatar
0 votes
0 answers
18 views

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 ...
Mr. Confused's user avatar
3 votes
0 answers
609 views

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 ...
NAAMA's user avatar
  • 39
2 votes
2 answers
792 views

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 ...
develarist's user avatar
  • 4,005
2 votes
1 answer
547 views

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 ...
Analyzer's user avatar
0 votes
0 answers
48 views

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?
j235's user avatar
  • 21
4 votes
1 answer
232 views

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, ...
KaPy3141's user avatar
  • 787
0 votes
1 answer
202 views

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 ...
Yohan Chung's user avatar
0 votes
0 answers
22 views

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 ...
Mehmet's user avatar
  • 1
1 vote
0 answers
164 views

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 ...
DuaneWhitney's user avatar
2 votes
2 answers
1k views

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 ...
Fortranner's user avatar
2 votes
1 answer
97 views

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 ...
Diogo Santos's user avatar
1 vote
1 answer
114 views

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 ...
Andrei1234's user avatar
2 votes
1 answer
220 views

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 ...
Folanir's user avatar
  • 127
2 votes
0 answers
72 views

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 ...
user59419's user avatar
  • 261
1 vote
1 answer
2k views

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 ...
Nitish Sherje's user avatar
1 vote
1 answer
1k views

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 ...
Econ_matrix's user avatar
0 votes
1 answer
529 views

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 ...
Raffles's user avatar
  • 121
2 votes
0 answers
1k views

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 ...
piove's user avatar
  • 81
0 votes
2 answers
119 views

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 ...
shshank92's user avatar
2 votes
1 answer
443 views

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 ...
Johnny_the_Quant's user avatar
1 vote
0 answers
62 views

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 ...
Math's user avatar
  • 25
1 vote
0 answers
454 views

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 ...
noFearOfBeer's user avatar
3 votes
1 answer
1k views

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 ...
J-H's user avatar
  • 257
4 votes
1 answer
220 views

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, ...
Alexander De Beur's user avatar
3 votes
3 answers
139 views

Arguments against model or forecast combination?

Do you know any references providing arguments against model or forecast (models output) combination? Could not find anything
Plazi's user avatar
  • 277
4 votes
2 answers
918 views

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 ...
Plazi's user avatar
  • 277
0 votes
2 answers
735 views

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%...
Emil Filipov's user avatar
10 votes
1 answer
2k views

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 ...
John Stella's user avatar
6 votes
1 answer
2k views

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 ...
Shudharsanan's user avatar
1 vote
1 answer
1k views

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 ...
kanimbla's user avatar
  • 584
3 votes
0 answers
1k views

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 ...
Łukasz Czop's user avatar
2 votes
0 answers
69 views

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 ...
lowndrul's user avatar
  • 2,147
2 votes
1 answer
45 views

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 ...
CooperBuckeye05's user avatar
9 votes
2 answers
4k views

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 ...
Jake's user avatar
  • 340
1 vote
2 answers
1k views

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?
Marine's user avatar
  • 173
49 votes
7 answers
23k views

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 ...
Biela Diela's user avatar
2 votes
2 answers
2k views

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 ...
nEO's user avatar
  • 151