scholarly journals PENGEMBANGAN MODEL PREDIKSI MADDEN JULIAN OSCILLATION (MJO) BERBASIS PADA HASIL ANALISIS DATA REAL TIME MULTIVARIATE MJO (RMM1 DAN RMM2) (PREDICTION MODEL DEVELOPMENT MADDEN JULIAN OSCILLATION (MJO) BASED ON THE RESULTS OF DATA ANALYSIS ...

Agromet ◽  
2008 ◽  
Vol 22 (2) ◽  
pp. 144 ◽  
Author(s):  
Lisa Evana ◽  
Sobri Effendy ◽  
Eddy Hermawan
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Outgoing Longwave Radiation ◽  
Time Series Data ◽  
Longwave Radiation ◽  
Empirical Orthogonal Functions ◽  
Wave Radiation ◽  
Series Data ◽  
Madden Julian Oscillation ◽  
Coverage Area

Background of this research is the importance of study on the Madden Julian Oscillation, the dominant oscillation in the equator area. MJO cycle showed by cloud cluster growing in the Indian Ocean then moved to the east and form a cycle with a range of 40-50 days and the coverage area from 10N-10S. Method that used to predict RMM is Box-Jenkins based on ARIMA (Autoregressive Integrated Moving Average) statistical analysis. The data used RMM daily data period 1 Maret 1979–1 Maret 2009 (30 years). RMM1 and RMM2 is an index for monitoring MJO. This is based on two empirical orthogonal functions (EOFs) from the combined average zonal 850hPa wind, 200hPa zonal wind, and satellite-observed Outgoing Longwave Radiation (OLR) data. The results in form of the Power Spectral Density (PSD) graph Real Time Multivariate MJO (RMM) and long wave radiation (OLR = Outgoing Longwave Radiation) at the position 100° BT, 120° BT, and 140°BT that show the wave pattern (spectrum pattern) and clearly shows the oscillation periods. There is a close relation between RMM1 with OLR at the position 100oBT that characterized the PSD value about 45 day. Through Box-Jenkins method, the prediction model that close to time series data of RMM1 and RMM2 is ARIMA (2,1,2), that mean the forecasts of RMM data for the future depending on one time previously and the error one time before. Prediction model for Zt = Zt = 1,681 Zt-1 – 0,722 Zt-2 - 0,02 at-1 - 0,05 at-2.. Prediction model for RMM2 is Zt = 1,714 Zt-1 – 0,764 Zt-2 - 0,109 at-1 - 0,05 at-2.. The flood case in Jakarta January-February 1996 and 2002 are one of real evidence that made the MJO prediction important. MJO with active phase dominant cover almost the entire Indonesia west area at that moment.

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

scholarly journals Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models

2020 ◽  
Vol 63 (5) ◽  
Author(s):  
Dulin Zhai ◽  
Xueming Zhang ◽  
Pan Xiong
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Time Series Data ◽  
Arima Model ◽  
Longwave Radiation ◽  
Time Series Forecasting ◽  
Series Data ◽  
Jiuzhaigou Earthquake ◽  
Forecasting Models ◽  
Mexico Earthquake

  The catastrophic damages caused by the Jiuzhaigou earthquake in China of August 8, 2017 and the Mexico earthquake of September 20, 2017 have revealed some important weaknesses of currently operational earthquake-monitoring and forecasting systems. In this work, six time series forecasting models were applied to detect pre-earthquake anomalies within infrared outgoing longwave radiation. After comparing their prediction results using non-seismic time series data, the autoregressive integrated moving average (ARIMA) model was selected as the optimal model, and then a new prediction method based on this ARIMA model was proposed. The results show that the values observed on July 27 and August 5 before the Jiuzhaigou earthquake in China exceed the confidence interval for prediction and reaches the maximum on August 5, 2017. This indicates the infrared outgoing longwave radiation (IR-OLR) anomalies before the Jiuzhaigou earthquake in China. For the Mexico earthquake, pre-earthquake IR-OLR anomalies are detected on September 14, 18, and 19, and reaches the maximum on September 14, 2017. This demonstrates that the proposed time series forecasting model based on ARIMA could be an effective method for earthquake anomalies detection within infrared outgoing longwave radiation.

scholarly journals Latitude–Height Structure of the Atmospheric Angular Momentum Cycle Associated with the Madden–Julian Oscillation

2007 ◽  
Vol 135 (4) ◽  
pp. 1564-1575 ◽  
Author(s):  
Joseph Egger ◽  
Klaus Weickmann
Keyword(s):  
Angular Momentum ◽  
Principal Components ◽  
Outgoing Longwave Radiation ◽  
Opposite Sign ◽  
Longwave Radiation ◽  
Empirical Orthogonal Functions ◽  
Madden Julian Oscillation ◽  
Orthogonal Functions ◽  
Tropical Troposphere ◽  
Global Mean

Abstract The angular momentum cycle of the Madden–Julian oscillation is analyzed by regressing the zonally averaged axial angular momentum (AAM) budget including fluxes and torques against the first two principal components P1 and P2 of the empirical orthogonal functions (EOFs) of outgoing longwave radiation (OLR). The maximum of P1 coincides with an OLR minimum near 150°E and a shift from anomalously negative AAM to positive AAM in the equatorial troposphere. AAM anomalies of one sign develop first in the upper-equatorial troposphere and then move downward and poleward to the surface of the subtropics within two weeks. During the same time the opposite sign AAM anomaly develops in the upper-equatorial troposphere. The tropical troposphere is warming when P1 approaches its maximum while the stratosphere is cooling. The torques are largest in the subtropics and are linked with the downward and poleward movement of AAM anomalies. The evolution is conveniently summarized using a time–height depiction of the global mean AAM and vertical flux anomaly.

A Comparison of OLR and Circulation-Based Indices for Tracking the MJO

2014 ◽  
Vol 142 (5) ◽  
pp. 1697-1715 ◽  
Author(s):  
George N. Kiladis ◽  
Juliana Dias ◽  
Katherine H. Straub ◽  
Matthew C. Wheeler ◽  
Stefan N. Tulich ◽  
...  
Keyword(s):  
Real Time ◽  
Outgoing Longwave Radiation ◽  
Seasonal Cycle ◽  
Large Scale ◽  
Longwave Radiation ◽  
Tropical Convection ◽  
Madden Julian Oscillation ◽  
Primary Interest ◽  
Large Scale Circulation ◽  
Reasonable Approximation

Abstract Two univariate indices of the Madden–Julian oscillation (MJO) based on outgoing longwave radiation (OLR) are developed to track the convective component of the MJO while taking into account the seasonal cycle. These are compared with the all-season Real-time Multivariate MJO (RMM) index of Wheeler and Hendon derived from a multivariate EOF of circulation and OLR. The gross features of the OLR and circulation of composite MJOs are similar regardless of the index, although RMM is characterized by stronger circulation. Diversity in the amplitude and phase of individual MJO events between the indices is much more evident; this is demonstrated using examples from the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign and the Year of Tropical Convection (YOTC) virtual campaign. The use of different indices can lead to quite disparate conclusions concerning MJO timing and strength, and even as to whether or not an MJO has occurred. A disadvantage of using daily OLR as an EOF basis is that it is a much noisier field than the large-scale circulation, and filtering is necessary to obtain stable results through the annual cycle. While a drawback of filtering is that it cannot be done in real time, a reasonable approximation to the original fully filtered index can be obtained by following an endpoint smoothing method. When the convective signal is of primary interest, the authors advocate the use of satellite-based metrics for retrospective analysis of the MJO for individual cases, as well as for the analysis of model skill in initiating and evolving the MJO.

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

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