In the present work, we investigate robust probabilistic forecasting models which aim to satisfy the both requirements. In the classical time series literature ...
This is the public repo for the paper "Robust Probabilistic Time Series Forecasting" (AISTATS '22). Requirements Recent versions of GluonTS, PyTorch, and ...
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
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What is probabilistic time series forecasting?
Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
What is an example of time series forecasting?
Examples of time series forecasting
Forecasting the closing price of a stock each day. Forecasting product sales in units sold each day for a store. Forecasting unemployment for a state each quarter. Forecasting the average price of gasoline each day.
How to do probabilistic forecasting?
Working with probabilistic forecasts
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Combine the uncertain production lead time with the uncertain transport lead time, to get the “total” uncertain lead time.
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Combine the uncertain demand with the uncertain lead time, to get the “total” uncertain demand to be covered by the stock about to be ordered.
What is the best statistical model for time series forecasting?
AutoRegressive Integrated Moving Average (ARIMA) models are among the most widely used time series forecasting techniques: In an Autoregressive model, the forecasts correspond to a linear combination of past values of the variable.
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
For this example we will use the classic Box-Cox AirPassengers dataset that we will augment it by introducing outliers. In particular, we will focus on ...
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Feb 14, 2024 · Bayesian time series forecasting is a powerful approach that provides flexible, robust, and interpretable forecasts.
Apr 4, 2024 · Example: Probabilistic Forecasting (Scenario: Medical Device). A medical device company focused on asthma care can leverage probabilistic ...
Apr 17, 2017 · We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. GENERAL FRAMEWORK. A critical ...
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time ...
Feb 1, 2023 · This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.