scholarly journals The Application of Spatial Analysis and Time Series in Modeling the Frequency of Earthquake Events in Bengkulu Province

2018 ◽  
Vol 7 (2) ◽  
pp. 103-114
Author(s):  
Fachri Faisal ◽  
Pepi Novianti ◽  
Jose Rizal
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Secondary Data ◽  
Spherical Model ◽  
Kriging Interpolation ◽  
Semivariogram Model ◽  
Series Analysis

This study provides an overview in combining spatial analysis and time series analysis to model the frequency of earthquake. The aim of this research is to apply the spatial statistical analysis and time series analysis in estimating semivariogram parameters for the next four steps. The data in this study is secondary data that has been validated based on sources that publish parameters of earthquake events. Looking at the characteristics of the earthquake frequency frequency data, there are spatial and time elements. The method used in this research is interpolation kriging and Autoregressive Moving Average (ARMA) model. The semivariogram models used in kriging interpolation are: Spherical, Exponential, Gaussian, and Linear. The parameters of the semivariogram model are modeled using ARMA time series analysis adjusted to the model diagnostic results. To measure of fit model is used Mean Square Error (MSE). The result of research is a suitable semivariogram model to be applied in the modeling of earthquake events is the Spherical model. While each parameter is estimated using ARMA model (2,2) with different coefficient estimation value.

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

The Deformation Prediction of Foundation Pit Slope Based on Time Series Analysis

2011 ◽  
Vol 80-81 ◽  
pp. 516-520
Author(s):  
Han Bing Liu ◽  
Yan Yi Sun ◽  
Yong Chun Cheng ◽  
Ping Jiang ◽  
Yu Bo Jiao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Information Criterion ◽  
Slope Deformation ◽  
Foundation Pit ◽  
Deformation Prediction ◽  
Series Analysis ◽  
Moving Average Model

Slope stability is the key to ensuring the safety of foundation pit construction. This paper is on the background of metro foundation pit monitoring of the West Railway Station in Changchun City. Through the time series analysis of the pit slope deformation data, the Auto Regressive Moving Average Model (ARMA) of pit slope deformation is established. Then the orders of the model are determined by the Akaike Information Criterion (AIC). Further, the deformation prediction of pit slope is finished using the ARMA model. By the comparison of the predictive value and the true monitoring value, it shows that using time series to analyze the deformation of foundation pit slope is reasonable and reliable. At the same time, this method is providing a new way to estimate the stability of pit slope.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

scholarly journals Impact of Crude Prices on Retail Prices – A Time Series Analysis

2019 ◽  
Vol 8 (2S7) ◽  
pp. 606-612
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
Series Analysis ◽  
Long Run ◽  
The Uk ◽  
The Impact

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

Time Series Analysis to Monitor and Assess Water Resources: A Moving Average Approach

2005 ◽  
Vol 109 (1-3) ◽  
pp. 65-72 ◽  
Author(s):  
Rajesh Reghunath ◽  
T. R. Sreedhara Murthy ◽  
B. R. Raghavan
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Time Series Analysis ◽  
Moving Average ◽  
Series Analysis

scholarly journals Penerapan WEMA dalam Peramalan Data IHSG

2013 ◽  
Vol 5 (2) ◽  
pp. 63-66
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Average Method ◽  
Stock Exchange ◽  
Moving Average ◽  
Composite Index ◽  
Market Change ◽  
Series Analysis ◽  
Future Data ◽  
Technical Indicator

One of the most popular technical indicator used in time series analysis for predicting future data is the Moving Average method. During its’ development, many variation and implementation have been made by researchers, one of them is the Weighted Exponential Moving Average (WEMA) which is introduced by Hansun.In this paper, we will try to implement the WEMA method on one of stock market change indicator in Indonesia, i.e. the Jakarta Stock Exchange (JKSE) composite index data. The research is continued by calculating the accuracy and robustness of WEMA method, using MSE and MAPE criteria. The result shows that the WEMA method can be used to predict JKSE data and it’s quite accurate. Kata kunci—time series analysis, JKSE, moving average, WEMA

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