Assessing the Effects of Terrorism on Tourism by Use of Time Series Methods

2003 ◽  
Vol 9 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Moving Average ◽  
Single Equation ◽  
Autoregressive Moving Average ◽  
Short Term ◽  
Explanatory Variables ◽  
Use Of Time ◽  
Dependent Variables ◽  
Armax Model

This paper presents an assessment of the effects of terrorism on tourism by using time series methods, namely the ARMAX (autoregressive moving average with explanatory variables) model. This is a single-equation approach, which has the ability to provide impact analysis easily. The use of the ARMAX model allows for the general shape of the lag distribution of the impacts of the explanatory variables based on the ratio of lag polynomials for the independent and dependent variables. The ARMAX models, like the ARIMA models, provide for a short-term assessment of terrorist incidents on tourism.

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

scholarly journals Influence of seasonality, salinity and temperature on catch trend of Penaeus indicus H. Milne-Edwards, 1837 in a coastal lagoon, India

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
R.K. Raman ◽  
V.R. Suresh ◽  
S.K. Mohanty ◽  
K.S. Bhatta ◽  
S.K. Karna ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Lagoons ◽  
Moving Average ◽  
Positive Influence ◽  
Chilika Lagoon ◽  
Explanatory Variables ◽  
Structural Time Series ◽  
Water Temperature Data ◽  
Penaeus Indicus ◽  
Auto Regressive

The catch pattern of P. indicus in coastal lagoons is influenced by seasonal changes in physicochemical parameters of the lagoon ecosystem. In this study the effects of seasonality, salinity and water emperature of lagoon on P. indicus catch were analysed using Structural Time Series Model (STSM) and ARIMAX (Auto Regressive Integrated Moving Average with explanatory variables) modeling approach using monthly time series catch, salinity and water temperature data of the Chilika Lagoon (a Ramsar site) in India for the period from 2001 to 2015. Results showed a significant (p<0.05) increasing stochastic upward trend and two seasonal cycles for P. indicus catch in the lagoon. Salinity was found to have significant positive influence (p<0.05) and temperature to have insignificant positive influence on P. indicus catch in the lagoon.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

Load Forecasting using Time Series Techniques

2021 ◽  
pp. 11343-11357
Author(s):  
Shahida Khatoon, Ibraheem, Priti, Mohammad Shahid
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Trend ◽  
Moving Average ◽  
Load Forecasting ◽  
Electric Load ◽  
Time Data ◽  
Efficient Operation ◽  
Use Of Time ◽  
Distribution Company

Load Forecasting is of great significance for effective and efficient operation of power system. Use of time series is of much importance in load forecasting. In this study, effectiveness of different time series techniques is identified to gathered valuable information. The objective is to predict electric load efficiently and effectively. This paper analyses the prediction accuracy of variety of time series method in modeling Electric load forecasts. The study examines the time series forecasting methods applied to estimate future electric load, specifically, Moving Average (MA), Linear Trend, the Exponential and Parabolic Trend. A comparison of different forecasting techniques of Time Series is demonstrated on real time data. The data utilized for forecast is made available through a distribution company of India. The traditional linear models and hybrid models along with ANN are developed. These models are appraised for the forecasting capability.

ARMA processes have maximal entropy among time series with prescribed autocovariances and impulse responses

1985 ◽  
Vol 17 (04) ◽  
pp. 810-840 ◽  
Author(s):  
Jürgen Franke
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Moving Average ◽  
Autoregressive Process ◽  
Natural Generalization ◽  
Maximal Entropy ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Arma Processes

The maximum-entropy approach to the estimation of the spectral density of a time series has become quite popular during the last decade. It is closely related to the fact that an autoregressive process of order p has maximal entropy among all time series sharing the same autocovariances up to lag p. We give a natural generalization of this result by proving that a mixed autoregressive-moving-average process (ARMA process) of order (p, q) has maximal entropy among all time series sharing the same autocovariances up to lag p and the same impulse response coefficients up to lag q. The latter may be estimated from a finite record of the time series, for example by using a method proposed by Bhansali (1976). By the way, we give a result on the existence of ARMA processes with prescribed autocovariances up to lag p and impulse response coefficients up to lag q.

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