A threshold mixed count time series model: estimation and application

2019 ◽  
Vol 24 (2) ◽  
Author(s):  
Mardi Dungey ◽  
Vance L. Martin ◽  
Chrismin Tang ◽  
Andrew Tremayne
Keyword(s):  
Time Series ◽  
Global Financial Crisis ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Efficient Method Of Moments ◽  
The Great Depression ◽  
Autoregressive Moving Average Model ◽  
Global Banking ◽  
Method Of Moments Estimator ◽  
Over Time

AbstractA new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.

Time series modelling — an integrated approach

1982 ◽  
Vol 19 (A) ◽  
pp. 413-425
Author(s):  
Don McNeil
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Integrated Approach ◽  
Autoregressive Moving Average ◽  
Linear Segment ◽  
Economic Data ◽  
Autoregressive Moving Average Model ◽  
Straight Line ◽  
Time Series Modelling ◽  
Moving Average Model

Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.

scholarly journals Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jia Li ◽  
Yunni Xia ◽  
Xin Luo
Keyword(s):  
Time Series ◽  
Petri Net ◽  
Response Times ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Firing Rates ◽  
Dynamic Framework ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Service Components

OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.

Modeling and Forecasting Sales Data by Time Series Analysis

1981 ◽  
Vol 18 (1) ◽  
pp. 94-100 ◽  
Author(s):  
S. G. Kapoor ◽  
P. Madhok ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time Series Modeling ◽  
Average Model ◽  
Sales Data ◽  
Autoregressive Moving Average Model ◽  
Deterministic Function ◽  
Modeling And Forecasting ◽  
Moving Average Model

Time series modeling technique is used to model a series of sales data in which seasonality causes distinct spike peaks. The analysis of actual sales data shows that the seasonality in the data can be approximated by a deterministic function and the stochastic component is a sixth-order autoregressive moving average model. Use of the combined deterministic and stochastic models to derive the minimum mean squared forecast yields reliable results.

Health Prognostic Method Based on the Time Series Analysis for Actuators

2013 ◽  
Vol 373-375 ◽  
pp. 329-332 ◽  
Author(s):  
Jing Kai Zhang ◽  
Juan Wang ◽  
Xiao Xiong Liu ◽  
Wei Guo Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Flight Control ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Health State ◽  
Future Health ◽  
Series Analysis ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

The purpose of health prognostic is to predict the future health status of system and determine the time from the current health state to functional failure completely. Application data time series analysis method often can get the expected prediction effect. Taking into account the failure characteristics of the actuators in flight control system, the autoregressive moving average model is introduced to health prognostic. The prognostic model is established. The simulation results show the effectiveness of the algorithm.

Time series modelling — an integrated approach

1982 ◽  
Vol 19 (A) ◽  
pp. 413-425
Author(s):  
Don McNeil
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Integrated Approach ◽  
Autoregressive Moving Average ◽  
Linear Segment ◽  
Economic Data ◽  
Autoregressive Moving Average Model ◽  
Straight Line ◽  
Time Series Modelling ◽  
Moving Average Model

Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.

Estimation and Forecast Performance of a Multivariate Time Series Model of Sales

1980 ◽  
Vol 17 (4) ◽  
pp. 558-565 ◽  
Author(s):  
Mark Moriarty ◽  
Gerald Salamon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Substantial Improvement ◽  
Product Line ◽  
Time Series Model ◽  
Autoregressive Moving Average ◽  
Forecast Performance ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

A unique form of a multivariate time series model—a “seemingly unrelated autoregressive moving average” model (SURARMA)—is developed in the context of forecasting unit sales of a product in four states. Data from an anonymous firm are used to test the appropriateness of the model and are found to conform to the model's constraints. The model provides substantial improvement in parameter estimation efficiency and forecast performance in comparison with individual state univariate models. SURARMA is potentially relevant to many market forecasting problems involving multiple constituent time series subunits such as states, regions, or products from a product line.

scholarly journals Analysis and prediction of polar motion using MSSA method

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Polar Motion ◽  
Rotation Axis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Rotational Axis ◽  
Autoregressive Moving Average Model ◽  
System Service ◽  
Moving Average Model ◽  
Polar Motion Prediction

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.

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