scholarly journals Bayesian Dynamic Linear Model with Adaptive Parameter Estimation for Short-Term Travel Speed Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Tai-Yu Ma ◽  
Yoann Pigné
Keyword(s):  
Linear Model ◽  
Time Series Data ◽  
Travel Speed ◽  
Series Data ◽  
Model Parameters ◽  
Prediction Errors ◽  
System Behavior ◽  
Dynamic Linear Model ◽  
Short Term ◽  
Speed Prediction

Bayesian dynamic linear model is a promising method for time series data analysis and short-term forecasting. One research issue concerns how the predictive model adapts to changes in the system, especially when shocks impact system behavior. In this study, we propose an adaptive dynamic linear model to adaptively update model parameters for online system state prediction. The proposed method is an automatic approach based on the feedback of prediction errors at each time slot without the needs of external intervention. The experimental study on short-term travel speed prediction shows that the proposed method can significantly reduce the prediction errors of the traditional dynamic linear model and outperform two state-of-the-art methods in the case of major system behavior changes.

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Michael Hauser ◽  
Yiwei Fu ◽  
Shashi Phoha ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Probability Distributions ◽  
Series Data ◽  
Model Parameters ◽  
Short Term ◽  
Term Memory ◽  
Experimental Apparatus ◽  
Long Short Term Memory

This paper makes use of long short-term memory (LSTM) neural networks for forecasting probability distributions of time series in terms of discrete symbols that are quantized from real-valued data. The developed framework formulates the forecasting problem into a probabilistic paradigm as hΘ: X × Y → [0, 1] such that ∑y∈YhΘ(x,y)=1, where X is the finite-dimensional state space, Y is the symbol alphabet, and Θ is the set of model parameters. The proposed method is different from standard formulations (e.g., autoregressive moving average (ARMA)) of time series modeling. The main advantage of formulating the problem in the symbolic setting is that density predictions are obtained without any significantly restrictive assumptions (e.g., second-order statistics). The efficacy of the proposed method has been demonstrated by forecasting probability distributions on chaotic time series data collected from a laboratory-scale experimental apparatus. Three neural architectures are compared, each with 100 different combinations of symbol-alphabet size and forecast length, resulting in a comprehensive evaluation of their relative performances.

scholarly journals ARX Linear Model Set-Up for Fault Diagnosis of Gas Turbine Sensors

1997 ◽  
Author(s):  
R. Bettocchi ◽  
P. R. Spina
Keyword(s):  
Fault Diagnosis ◽  
System Identification ◽  
Gas Turbine ◽  
Linear Model ◽  
Time Series Data ◽  
System Modeling ◽  
Series Data ◽  
Model Parameters ◽  
Model Set ◽  
Set Up

The diagnosis of gas turbine sensor faults requires models of the system to calculate estimates of the measured output system variables. The model set-up phase is of great importance since the reliability of the diagnostic tool depends on the model accuracy. In the paper two different methodologies of I/O linear model set-up are analyzed and compared to find the more simple and general one. The first methodology consists in obtaining the I/O linear models by directly linearizing the physical laws (system modeling). The second one uses statistical methods (system identification) to calculate model parameters from time series data measured on the machine. The models used are of the ARX (Auto Regressive with eXternal input) type. The number of models and the measured variables correlated by each of them have been determined in order to obtain unambiguous fault signatures for each sensor. The system identification method proves to be more suitable to the system modeling because of its greater simplicity in the fault diagnosis application.

G-CNN and double-referenced thresholding for detecting time series anomalies

2020 ◽  
pp. 1-12
Author(s):  
Liping Li ◽  
Zean Tian ◽  
Kenli Li ◽  
Cen Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Feedback Information ◽  
Public Datasets ◽  
Gated Recurrent Units ◽  
Control Feedback ◽  
Intermediate Output ◽  
Internet Company

Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

scholarly journals THE TESTING OF MONEY NEUTRALITY IN ECONOMIC GROWTH OF INDONESIA

2017 ◽  
Vol 1 (1) ◽  
pp. 12
Author(s):  
Muammil Sun’an ◽  
Amran Husen
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Output Growth ◽  
Broad Sense ◽  
Series Data ◽  
Analysis Tool ◽  
Real Sector ◽  
Short Term ◽  
Correction Model

<p>This study aim is to test the money neutrality in a narrow sense (M1) and a broad sense (M2) to the growth of output (GDP) in Indonesia, both in short term and long term. This research uses quarterly time series data at 2010 - 2016 periods. The analysis tool used is Error Correction Model (ECM). The results show that short-term money supply (M1 and M2) affect on output growth. However, in the long term, only money circulation in a broad sense (M2) affects on output growth, which also means that money is not neutral because it affects the real sector (GDP).</p><p> <strong>Keywords:</strong> M1, M2, Population, Capital, and Economic Growth.</p>

scholarly journals Integration of Indonesian Coffee Markets with World Coffee Markets

2021 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Haposan Orlando Napitupulu ◽  
Ana Arifatus Sa'diyah ◽  
Farah Mutiara
Keyword(s):  
Time Series Data ◽  
Dynamic Equilibrium ◽  
Secondary Data ◽  
Series Data ◽  
Short Term ◽  
Robusta Coffee ◽  
Long Run ◽  
Relationship Of ◽  
Coffee Markets

This study aims to analyze the integration of the Arabica and Robusta coffee markets in Indonesia with world coffee prices. The study uses secondary data in the form of annual time series data during the period 1985 - 2015. The study uses the VECM analysis method. This method explains the relationship of long-term dynamic equilibrium and short-term equilibrium in a system of equations. The analysis shows that Indonesian and world Arabica coffee is not integrated in the long term or the short term. In Robusta coffee VECM estimation analysis shows that there is a significant value at the 10% level in a long-term relationship with a value of 0.08579, which means that there is a short-term relationship between world Robusta coffee prices and domestic Robusta coffee prices in the previous year, but no relationship in the long run.

scholarly journals Analisis Peningkatan Upah Minimum Terhadap Penyerapan Tenaga Kerja di Provinsi Jawa Tengah

2021 ◽  
Vol 10 (3) ◽  
pp. 134-143
Author(s):  
Annisa Yulianti ◽  
Hadi Sasana
Keyword(s):  
Economic Growth ◽  
Minimum Wage ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Central Java ◽  
Meaningful Relationship ◽  
Short Time ◽  
Relationship Of

 This study aims to analyze the short-term and long-term relationship of increasing the minimum wage in Central Java on employment. The research method used is ECM. The variables of this study include labor, minimum wages, PMDN, and economic growth. The data used are time-series data from 1990-2020. The results show that the minimum wage has a positive and significant relationship to the employment in the long term but not significantly in the short time. PMDN has a negative but significant correlation in the short and long term. At the same time, the variable economic growth has a positive but not meaningful relationship to employment absorption in the long and short term.

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