Questions tagged [probabilistic-forecasts]
The probabilistic-forecasts tag has no usage guidance.
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Using Bootstrapped Residuals to Estimate Time Series Prediction Intervals
I am working with a very simple forecasting "model" which is not a standard statistical model. I am trying to use the methodology described in Hyndman's textbook under the section "...
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Prediction Intervals for forecasts vs Probabilistic Forecasting
In the context of time series forecasting, there a relationship between these 2? Are prediction intervals are type of probabilistic forecasting?
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Can we get probabilistic predictions evaluable by proper scoring rules from bayesian inference without evaluating the marginal likelihood?
Let's say we have a vector of inputs, $X=[x_0,\dots, x_{n-1}]$, and a vector of outputs, $Y=[y_0, \dots, y_{n-1}]$.
We would like to predict the distribution of a new output ,$\hat{y}$, given a new ...
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Backtransforming a probabilistic forecast?
Let's say that we have a probabilistic forecast for the future percentage return of an asset in the form of a probability density, $\hat{R}_{t+1}$.
If our initial goal was to create a probabilistic ...
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Triplets, Sampling Criteria and Variance?
Say I have a population size of 1000 students from schools in different towns (assume they are in the same class year, of mixed gender, same country/state). The mean, variance and standard deviation ...
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Resources for Probabilstic forecasting
I was planning on learning probabilistic forecasting and im completely lost. Suggest some online resources. I have started with Probabilistic Forecasting and Bayesian Data Assimilation but its a bit ...
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Is there a closed form for multi-step ARIMA/ARMA density forecasts conditioned on initial values?/alternatives to this?
I am attempting to create a benchmark for probabilistic forecasting of time series to test other models against and figured that a linear ARIMA/ARMA model would be a good starting point.
I thought ...
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Is generation/evaluation of probabilistic predictions on continuous data feasible for larger data sets in practice?
To better capture uncertainty about the phenomena that we model, probabilistic predictions seem to be a natural and common extension of point predictions.
Methods for evaluation of these predictions ...
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Do "types" of predictions "need" to match assumptions about data?
Let $\mathcal X$ (observation space) and $\mathcal P$ (prediction space) be non-empty sets.
Let $P$ be a variadic function (predictive model) mapping tuples of elements of $\mathcal X$ into $\mathcal ...
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Problem formulation of future timeframe prediction based on current time
I have a problem where I want to predict "when is the next action happening" based on the time.
Example problem: Imagine you have a dataset of transactions per user, your goal is to predict ...
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How to assess calibration of probability distribution for a multiclass model?
I have a multiclass classifier (boosting model), and my goal is to have a good approximation of the actual distribution to the classes given my feature values.
I.e. suppose I have features $X$, and ...
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Should Bayesian probability of model given the data coincide with intuitive estimation of this probability?
I have $N$ real valued targets ordered by one single real valued feature. I would like to build a simple probabilistic model that states that all the targets corresponding to the features that are ...
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Scale-Invariant CRPS Alternative
I am currently working on a probabilistic forecasting problem (outputting the full predictive distribution, possibly in the form of samples) and I need to decide on a measure to evaluate the forecasts....
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How can I generate probabilistic forecasts to do probabilistic classification?
I have a collection of univariate, irregularly spaced, financial time series. Each series is labeled by its class. The image below shows some example data.
A note on the data:
The time series could ...
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One-vs-rest vs one-vs-one multiclass probability validation: does it matter?
Now that I have figured out how rms::val.prob works to the extent that I have written my own Python implementation, I would like to extend that idea to multiple ...
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Predictive distribution in the Frequentist setting
Let $\{X_t\}_{t \in T}$ be a time series, such that $X \sim F(\theta)$, for some arbitrary distribution $F$. Based on the observed values $\{x_1, x_2, \cdots, x_{t-1}\}$, suppose that I want to ...
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How to combine state-level COVID-19 vaccination rates with national demographic data at the individual level?
I’m investigating possible correlations between the COVID-19 vaccination rate in the United States and the results of a long-running survey of scientific personality traits like "Agreeableness&...
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Computing probabilistic forecasts for a ratio of two other given forecasts
I have three time series (Revenue, Volume and Price), with Price = Revenue / Volume.
I have created probabilistic forecasts for Revenue and Volume and now need to derive a consistent forecast (...
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Nonlinear regression: What's the best estimator for this problem?
Context and goals:
Consider $x$ a scalar (deterministic) independent variable and $y = f(x;\beta)+\eta$ be a dependent random variable obtained trough $f$ with some parameters $\beta$ and $\eta$ is ...
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Which algorithm suits for classification of multivariate time series data?
I have multivariate time series data, where the goal is to predict a binary label, which is changing in time. For illustration: there are 20 individuals, for each of them I measure 50 values (...
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What method would I use when data has an upper and lower boundary in GluonTS
I have a time series dataset that has a max ceiling of 50 and a lower threshold of 0, however when using probabilistic time series modeling like GluonTS I am getting future predictions that are either ...
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Distribution that acts like Poisson/NegBin for small means and like a Normal distribution for large means?
I want to generate a full density probabilistic forecasting model, where I don't know a priori whether the time series I want to model are intermittent or dense. In both cases, the time series is a ...
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