scholarly journals Nowcasting Austrian Short Term Statistics

2018 ◽  
Vol 34 (2) ◽  
pp. 503-522
Author(s):  
Markus Fröhlich
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Seasonal Adjustment ◽  
Short Term ◽  
Vector Autoregressive ◽  
Adjustment Program ◽  
Time Series Approach ◽  
Total Turnover ◽  
Vector Error Correction Models

Abstract Early estimates for Austrian short term indices were produced using multivariate time-series models. The article presents a simulation study with different models (vector error correction models, vector autoregressive models in levels – both with unadjusted and seasonally adjusted time-series) used for estimating total turnover, production, etc. In a preliminary step, before time-series were provided for nowcasting, the data had to undergo an editing process. In this case a time-series approach was selected for data-editing as well, because of the very specific structure of Austrian enterprises. For this task basically the seasonal adjustment program X13Arima-Seats was used for identifying and replacing outlying observations, imputation of missing values and generating univariate forecasts for every single time series.

scholarly journals Compound Autoregressive Network for Prediction of Multivariate Time Series

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuting Bai ◽  
Xuebo Jin ◽  
Xiaoyi Wang ◽  
Tingli Su ◽  
Jianlei Kong ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Prediction Algorithm ◽  
Short Term ◽  
Advanced Control ◽  
Multiple Variables ◽  
Nonlinear Autoregressive Model ◽  
Interactive Relation ◽  
Nonlinear Autoregressive

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

scholarly journals Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

2020 ◽  
Vol 34 (04) ◽  
pp. 5956-5963
Author(s):  
Xianfeng Tang ◽  
Huaxiu Yao ◽  
Yiwei Sun ◽  
Charu Aggarwal ◽  
Prasenjit Mitra ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Missing Values ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Temporal Distribution ◽  
Joint Modeling ◽  
Adversarial Training ◽  
Global Patterns ◽  
Real World Datasets

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.

scholarly journals DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

2020 ◽  
Vol 34 (01) ◽  
pp. 930-937
Author(s):  
Qingxiong Tan ◽  
Mang Ye ◽  
Baoyao Yang ◽  
Siqi Liu ◽  
Andy Jinhua Ma ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Medical Knowledge ◽  
Attention Mechanism ◽  
Physiological Variables ◽  
Real World Datasets ◽  
Clinical Records ◽  
Gated Recurrent Unit ◽  
Time Aware

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

scholarly journals Recurrent Neural Networks for Multivariate Time Series with Missing Values

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhengping Che ◽  
Sanjay Purushotham ◽  
Kyunghyun Cho ◽  
David Sontag ◽  
Yan Liu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Missing Values ◽  
Multivariate Time Series
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