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  1. AU=Veit Kaleb
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  1. Article: Pseudothrombotic microangiopathy and vitamin B12 deficiency in pernicious anemia.

    Veit, Kaleb

    Proceedings (Baylor University. Medical Center)

    2016  Volume 30, Issue 3, Page(s) 346–347

    Abstract: Vitamin B12 deficiency may present as pseudothrombotic microangiopathy. Pseudothrombotic microangiopathy is anemia, thrombocytopenia, and schistocytosis caused by vitamin B12 deficiency. Pseudothrombotic microangiopathy may be mistaken for ... ...

    Abstract Vitamin B12 deficiency may present as pseudothrombotic microangiopathy. Pseudothrombotic microangiopathy is anemia, thrombocytopenia, and schistocytosis caused by vitamin B12 deficiency. Pseudothrombotic microangiopathy may be mistaken for microangiopathic hemolytic anemia, confounding appropriate treatment. Evaluation of lactate dehydrogenase, reticulocyte count, bilirubin, and platelet count are most helpful in differentiating pseudothrombotic microangiopathy from a true microangiopathic hemolytic anemia. A case of pseudothrombotic microangiopathy due to severe vitamin B12 deficiency is presented.
    Language English
    Publishing date 2016-01-05
    Publishing country United States
    Document type Case Reports
    ZDB-ID 2703932-8
    ISSN 1525-3252 ; 0899-8280
    ISSN (online) 1525-3252
    ISSN 0899-8280
    DOI 10.1080/08998280.2017.11929643
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks

    Phipps, Kaleb / Heidrich, Benedikt / Turowski, Marian / Wittig, Moritz / Mikut, Ralf / Hagenmeyer, Veit

    2023  

    Abstract: In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these ... ...

    Abstract In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these deterministic forecasts into probabilistic forecasts is often challenging and based on numerous assumptions that may not hold in real-world situations. Therefore, the present article proposes a novel approach for creating probabilistic forecasts from arbitrary deterministic forecasts. In order to implement this approach, we use a conditional Invertible Neural Network (cINN). More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions. Besides showing the mathematical validity of our approach, we empirically show that our approach noticeably outperforms traditional methods for including uncertainty in deterministic forecasts and generally outperforms state-of-the-art probabilistic forecasting benchmarks.

    Comment: Preprint submitted to the International Journal of Forecasting
    Keywords Computer Science - Machine Learning
    Subject code 333
    Publishing date 2023-02-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: ProbPNN

    Heidrich, Benedikt / Phipps, Kaleb / Neumann, Oliver / Turowski, Marian / Mikut, Ralf / Hagenmeyer, Veit

    Enhancing Deep Probabilistic Forecasting with Statistical Information

    2023  

    Abstract: Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven ... ...

    Abstract Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-02-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Review of automated time series forecasting pipelines

    Meisenbacher, Stefan / Turowski, Marian / Phipps, Kaleb / Rätz, Martin / Müller, Dirk / Hagenmeyer, Veit / Mikut, Ralf

    2022  

    Abstract: Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process ... ...

    Abstract Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.
    Keywords Computer Science - Machine Learning
    Subject code 330
    Publishing date 2022-02-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Evaluating Ensemble Post-Processing for Wind Power Forecasts

    Phipps, Kaleb / Lerch, Sebastian / Andersson, Maria / Mikut, Ralf / Hagenmeyer, Veit / Ludwig, Nicole

    2020  

    Abstract: Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can ...

    Abstract Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biased and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biases in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles, and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness, whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance.

    Comment: Accepted by Wiley Wind Energy on April 19, 2022
    Keywords Statistics - Applications
    Subject code 333
    Publishing date 2020-09-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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