Study on Four Parameters Estimation’s Methods of Time-Series Model in Modeling the Signal of a Quartz Flex Accelerometer

2011 ◽  
Vol 128-129 ◽  
pp. 329-332
Author(s):  
Xin Zhong ◽  
Chi Hang Zhao ◽  
Jie He
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Test Bench ◽  
Time Series Data ◽  
Least Square Method ◽  
Least Square ◽  
Statistical Characteristics ◽  
Estimation Methods ◽  
Series Data ◽  
Partial Autocorrelation Function

Box-Jenkins method of time series in modeling the signal of quartz flex accelerometer is studied in the paper. Firstly, a JSD-I/A quartz flex accelerometer is placed on a level test bench, and the output signal of the JSD-I/A quartz flex accelerometer is acquired. Secondly, the acquired signal of the JSD-I/A quartz flex accelerometer is preprocessed by Db3 wavelet denoising, trend items exacting, and standardized processing. Thirdly, The statistical characteristics of autocorrelation function (ACF) and partial autocorrelation function (PACF) of the processed time series data are analyzed, and the results show that ACF presents tailing characteristic and PACF presents censoring characteristic after 12th order. So AR(12) model is suitable for modeling the processed data of the JSD-I/A quartz flex accelerometer. Fourthly, the AR(12) model’s parameters are estimated by four methods, named least square method (LSM), Yule-Walker method, LUD method and Burg method, respectively. The fitting effects by residuals sum of squares (RSS) of the above estimation methods are compared and the results show that LSM outperforms the other three estimation methods.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR UDANG PUTIH (PENAEUS INDICUS) INDONESIA KE HONGKONG SERTA IMPLIKASI KEBIJAKANNYA

2018 ◽  
Vol 5 (1) ◽  
pp. 1-24
Author(s):  
Betrix Silitonga ◽  
Manuntun Parulian Hutagaol
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Least Square Method ◽  
Least Square ◽  
Nominal Exchange Rate ◽  
White Shrimp ◽  
Series Data ◽  
Selling Price ◽  
Ordinary Least Square ◽  
Penaeus Indicus

This study aimed to analyze the factors that affect the volume of Indonesian exports of white shrimp (Penaeus indicus) to Hongkong and to formulate policies that can increase the volume of exports. Ordinary Least Square method (OLS) is used to analyze the factors that affect the volume of Indonesian white shrimp exports to Hongkong by time series data (time series) period of the last 24 years (1990 until 2013). All independent variables that have significant influence on the volume of Indonesian exports of white shrimp, which are: Hongkong population, Hongkong GDP per capita constant 2005, the nominal selling price of white shrimp Indonesia to Hongkong, the nominal selling price of white shrimp competitor to Hongkong, the nominal exchange rate of the Indonesian to Hongkong and Indonesian economic crisis in 1998 (dummy). Key words: Hongkong, Ordinary Least Square (OLS), the export volume, the white shrimp (Penaeus indicus)

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

scholarly journals The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 69
Author(s):  
Guoliang Feng ◽  
Wei Lu ◽  
Jianhua Yang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Least Square ◽  
Fuzzy Cognitive Map ◽  
Statistical Characteristics ◽  
Series Data ◽  
The Impact

A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time series. A fuzzy c-means clustering algorithm is used to construct the concepts of the FCM. Compared with the traditional FCM, the least square fuzzy cognitive map (LSFCM) is a direct solution procedure without iterative calculations. LSFCM model is a straightforward, robust and rapid learning method, owing to its reliable and efficient. In addition, the structure of the LSFCM can be further optimized with refinements the position of the concepts for the higher prediction precision, in which the evolutionary optimization algorithm is used to find the optimal concepts. Withal, we discussed in detail the number of concepts and the parameters of activation function on the impact of FCM models. The publicly available time series data sets with different statistical characteristics coming from different areas are applied to evaluate the proposed modeling approach. The obtained results clearly show the effectiveness of the approach.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR UDANG PUTIH (PENAEUS INDICUS) INDONESIA KE HONGKONG SERTA IMPLIKASI KEBIJAKANNYA

2018 ◽  
Vol 5 (1) ◽  
pp. 1-24
Author(s):  
Betrix Silitonga ◽  
Manuntun Parulian Hutagaol
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Least Square Method ◽  
Least Square ◽  
Nominal Exchange Rate ◽  
White Shrimp ◽  
Series Data ◽  
Selling Price ◽  
Ordinary Least Square ◽  
Penaeus Indicus

This study aimed to analyze the factors that affect the volume of Indonesian exports of white shrimp (Penaeus indicus) to Hongkong and to formulate policies that can increase the volume of exports. Ordinary Least Square method (OLS) is used to analyze the factors that affect the volume of Indonesian white shrimp exports to Hongkong by time series data (time series) period of the last 24 years (1990 until 2013). All independent variables that have significant influence on the volume of Indonesian exports of white shrimp, which are: Hongkong population, Hongkong GDP per capita constant 2005, the nominal selling price of white shrimp Indonesia to Hongkong, the nominal selling price of white shrimp competitor to Hongkong, the nominal exchange rate of the Indonesian to Hongkong and Indonesian economic crisis in 1998 (dummy). Key words: Hongkong, Ordinary Least Square (OLS), the export volume, the white shrimp (Penaeus indicus)

scholarly journals Employment and foreign direct investment

2018 ◽  
Vol 69 (5) ◽  
pp. 552-570
Author(s):  
Ana Gardašević
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Econometric Model ◽  
Statistical Significance ◽  
Least Square Method ◽  
Least Square ◽  
Series Data ◽  
Foreign Direct Investments ◽  
Total Structure

The purpose of this study is to examine and better understand the effects of foreign direct investments on employment in Montenegro over the period from 2005-2017. Time series data on a quarterly basis, obtained from Central Bank of Montenegro, Statistical Office of Montenegro - MONSTAT and Employment Office of Montenegro were used in this research. To perform time series stationary testing, Dickey-Fuller test (ADF) and Perron test were used, i.e. the unit root tests, while the examination of the effects of foreign direct investments on employment was performed using the regression analysis with the least square method. The results of the research, obtained by the evaluation of the simple linear econometric model show that if foreign direct investments increase by 1%, employment in Montenegro can be expected to increase by an average of 0.0058%. However, the regression analysis results clearly show that over the total observed period, the influence of foreign direct investments on employment does not have statistical significance. The obtained results are not surprising, considering the fact that the observed period is characterized by a modest share of the greenfield investment within the total structure of foreign direct investments in the Montenegrin economy.

scholarly journals ANALISIS PENGARUH INVESTASI SWASTA , PENGELUARAN PEMERINTAH, DAN PENDUDUK TERHADAP PERTUMBUHAN EKONOMI DI KOTA BITUNG

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yandiles Weya ◽  
Vecky A.J. Masinambow ◽  
Rosalina A.M. Koleangan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Economic Activity ◽  
Time Series Data ◽  
Private Investment ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square ◽  
North Sulawesi ◽  
The City

ANALISIS PENGARUH INVESTASI SWASTA , PENGELUARAN PEMERINTAH, DAN PENDUDUK TERHADAP PERTUMBUHAN EKONOMI DI KOTA BITUNG Yandiles Weya, Vecky A.J. Masinambow, Rosalina A.M. Koleangan. Fakultas Ekonomi dan Bisnis, Magister Ilmu EkonomiUniversitas Sam Ratulangi, Manado ABSTRAKPada suatu periode perekonomian mengalami pertumbuhan negatif berarti kegiatan ekonomi pada periode tersebut mengalami penurunan. Kota Bitung periode tahun 2004-2014 mengalami pertumbuhan ekonomi yang fluktuasi. Adanya fluktuasi ini dapat dipengaruhi oleh investasi swasta, belanja langsung, dan penduduk Pertumbuhan ekonomi merupakan salah satu tolok ukur keberhasilan pembangunan ekonomi di suatu daerah. Pertumbuhan ekonomi mencerminkan kegiatan ekonomi. Pertumbuhan ekonomi dapat bernilai positif dan dapat pula bernilai negatif. Jika pada suatu periode perekonomian mengalami pertumbuhan positif berarti kegiatan ekonomi pada periode tersebut mengalami peningkatan. Sedangkan jikaTahun 2004-2014 yang bersumber dari Badan Pusat Statistik Provinsi Sulut dan Kota Bitung. Metode analisis yang digunakan adalah model ekonometrik regresi berganda double-log (log-log) dengan metode Ordinary Least Square (OLS). Penelitian ini bertujuan untuk mengetahui apakah perkembangan investasi swasta, belanja langsung, dan penduduk berpengaruh terhadap pertumbuhan ekonomi Kota Bitung. Data yang dipakai menggunakan data time series periodeHasil regresi model pertumbuhan ekonomi dengan persamaan regresinya yaitu  LPDRB  =  - 4,445    +  0.036 LINV  +  0.049 LBL  +  2,229 LPOP.  Dari hasil tersebutmenunjukkan perkembangan investasi swasta, belanja langsung dan penduduk berpengaruh positif dan signifikan terhadap pertumbuhan ekonomi Kota Bitung.Kata Kunci :pertumbuhan ekonomi, belanja langsung, penduduk, regresi bergandaABSTRACT    The economy experienced a period of negative growth means economic activity in this period has decreased. Bitung-year period 2004-2014 economic growth fluctuations. These fluctuations can be influenced by private investment, direct spending, and population Economic growth is one measure of the success of economic development in an area. Economic growth reflects economic activity. Economic growth can be positive and can also be negative. If the economy experienced a period of positive growth means economic activity during the period has increased. Whereas if  years 2004-2014 are sourced from the Central Statistics Agency of North Sulawesi Province and Bitung. The analytical method used is an econometric model double-log regression (log-log) with Ordinary Least Square (OLS). This study aims to determine whether the development of private investment, direct spending, and population affect the economic growth of the city of Bitung. The data used using time series data period.    The results of the regression model of economic growth with the regression equation is LPDRB = - LINV 4.445 + 0.036 + 0.049 + 2.229 LPOP LBL. From these results show the development of private investment, direct expenditure and population positive and significant impact on economic growth of Bitung.Keywords: Economic growth, direct spending, population, regression.

Pengaruh pertumbuhan ekonomi, inflasi dan pengangguran terhadap tingkat kemiskinan di Sumatera Selatan

2019 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Novegya Ratih Primandari
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Time Series Data ◽  
Secondary Data ◽  
Poverty Rate ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square

This research aims to analyze effect of economic growth, inflation and Unemployment on the Rate of Poverty in the Province of South Sumatera. This research used secondary data in the form of time series data from 2001-2017. The method used quantitative approach by applying a linear regression model with OLS estimation Ordinary Least Square (OLS) method. The results of this study indicate that partially and simultaneously Economic Growth, Inflation and Unemployment have a significant effect on the Poverty Rate in the Province of South Sumatera.

scholarly journals Do the Monetary Policy Makers Follow Rules? Testing Taylor’s Rule for Nepal

2019 ◽  
Vol 42 (1-2) ◽  
pp. 34-42
Author(s):  
Khagendra Katuwal
Keyword(s):  
Interest Rate ◽  
Central Bank ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Least Square Method ◽  
Least Square ◽  
Series Data ◽  
Output Gap ◽  
Treasury Bills ◽  
Augmented Dickey Fuller

The study estimates Taylor’s rule for Nepal by using the annual time series data for the period of 1988-2018. As a requirement of Taylor's rule, the output gap has been estimated by using Hodric-Perscott filter. Consumer price index has been used as measure of inflation and 91-days treasury bills rate is taken as the proxy for the short-term interest rate set by central bank of Nepal. The ordinary least square method has been used to estimate the Taylor's equation The results show that. As Augmented Dickey-Fuller test shows that all  the variables used in this study are in level form. The results show that there is a positive relationship of T-bills rule with inflation output gap. Interest rate smoothing is found to be a major concern of central bank of Nepal but follows the Taylor’s rule partially.

scholarly journals PENGARUH PERTUMBUHAN EKONOMI TERHADAP KESEMPATAN KERJA DI PROVINSI SULAWESI TENGGARA

2018 ◽  
Vol 6 (2) ◽  
pp. 54
Author(s):  
Muhammad Nur Afiat
Keyword(s):  
Economic Growth ◽  
Quantitative Research ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Least Square Method ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Series Data ◽  
Employment Opportunities

This study was conducted with the aim to determine the effect of Economic Growth Rate on Employment Opportunities in Southeast Sulawesi Province 2000-2015. This research is a type of Quantitative research using secondary data in the form of time series data, ie from 2000-2015. Data source was obtained from Central Bureau of Statistics (BPS) and Bank Indonesia of Southeast Sulawesi Province. This study also uses multiple linear regression analysis tools with ordinary least square method (OLS) and then processed with application Eviews 8.0. The results of the study show that Economic Growth has a significant influence on Employment Opportunities in Southeast Sulawesi Province 2000-2015.

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