Robust Kalman filter for Tuberculosis Incidence Time Series Forecasting <ce:cross-ref refid='fn1'> <ce:sup loc='post'>?</ce:sup> </ce:cross-ref>

Andres L. Jutinico; Erika Vergara; Carlos Enrique Awad García; Maria Angélica Palencia; Alvaro David Orjuela-Ca?on

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Robust Kalman filter for Tuberculosis Incidence Time Series Forecasting ?

机译：用于结核病发生时间序列的强大卡尔曼滤波器？

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Governments must detect and treat people with tuberculosis, also prevent the uninfected community. In this sense, must promote the study of algorithms for the prediction of the epidemic trend. This paper addresses the forecasting of tuberculosis cases in Bogota, considering health surveillance system data from 2007-2020. Forecasts are obtained using the Kalman Filter and the Robust Kalman Filter. Results show better performance using the robust filter for six-week tuberculosis cases prediction.

机译：政府必须探测和治疗患有结核病的人，也可以防止未感染的社区。从这个意义上讲，必须促进对疫情预测的算法研究。本文介绍了2007 - 2020年的健康监测系统数据对波哥大结核病病例的预测。使用卡尔曼滤波器和强大的卡尔曼滤波器获得预测。结果显示使用六周结核病例预测的强大滤波器更好的性能。

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《IFAC PapersOnLine》 |2021年第15期|共6页
作者
Andres L. Jutinico; Erika Vergara; Carlos Enrique Awad García; Maria Angélica Palencia; Alvaro David Orjuela-Ca?on;
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TuberculosisTime SeriesForecastingKalman filterAuto-Regressive models;

机译：Tuberculosistime系列ForecastingKalman Filterauto返回模型;

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1. Model Predictive Control meets robust Kalman filtering * * This work has been partially supported by the FIRB project “Learning meets time” (RBFR12M3AC) funded by MIUR. [J] . Alberto Zenere, Mattia Zorzi IFAC PapersOnLine . 2017,第1期

机译：模型预测控制满足鲁棒的卡尔曼滤波 * * FIRB项目“学习遇见时间”（RBFR12M3AC ）由MIUR资助。
2. Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53 ? ? [J] . Caroline Moosmüller, Christopher J. Tralie, Mahdi Kooshkbaghi, IFAC PapersOnLine . 2021,第9期

机译：时间序列的周期性评分编码肿瘤抑制器P53的动态行为p53 ？？
3. Distributed Kalman filtering for Time-Space Gaussian Processes * * This work is supported by Progetto di Ateneo CPDA147754/14-New statistical learning approach for multi-agents adaptive estimation and coverage control. [J] . M. Todescato, A. Dalla Libera, R. Carli, IFAC PapersOnLine . 2017,第1期

机译：时空高斯过程的分布式卡尔曼滤波 * * Progetto di Ateneo CPDA147754 / 14支持这项工作-新的统计学习多主体自适应估计和覆盖控制的方法。
4. Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model [C] . Yinping Chen, Aiping Wu, Cuiling Wang, International Conference on Applied Science, Engineering and Technology . 2013

机译：肺结核发病率的时间序列分析：应用时间序列模型预测
5. ROBUST ADAPTIVE KALMAN FILTERING WITH UNKNOWN INPUTS (SIGNAL-PROCESSING). [D] . MOGHADDAMJOO, ALIREZA. 1986

机译：鲁棒的自适应卡尔曼滤波，输入未知（信号处理）。
6. An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province China [O] . Yongbin Wang, Chunjie Xu, Yuchun Li, 2020

机译：中国青海省结核病发生时间序列预测的Sarima-NNNAR先进的数据驱动混合模型
7. >Improving the efficient and robust uncertainty quantification in real-time flood forecasting using Polynomial chaos expansions and ensemble Kalman filter [O] . Jongho Kim, Tran Vinh 2018

机译：>利用多项式混沌扩展，提高实时洪水预测中的高效和稳健的不确定性量化和集合卡尔曼滤波器

Robust Kalman filter for Tuberculosis Incidence Time Series Forecasting ?

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