Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data

2017 ◽  
Vol 24 (5) ◽  
pp. 768-782 ◽  
Author(s):  
Stephanie T. Lane ◽  
Kathleen M. Gates
Keyword(s):  
Time Series ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Time Series Data ◽  
Series Data ◽  
Individual Level ◽  
Selection Of

Measurement of the Effects of Regional Policy Instruments by Means of Linear Structural Equation Models and Panel Data

1981 ◽  
Vol 13 (11) ◽  
pp. 1435-1448 ◽  
Author(s):  
H Folmer
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Structural Equation ◽  
Regional Policy ◽  
Structural Equation Models ◽  
Time Series Data ◽  
Policy Instruments ◽  
Equation Model ◽  
Series Data ◽  
Cross Sectional

Two kinds of problems have generally been encountered in measuring the effects of instruments of regional policy. The first concerns the construction of an adequate method, which should (a) take explicitly into account the multidimensional character of a regional profile, (b) disentangle policy effects from autonomous developments, (c) make it possible to compare the effects of different policy instruments, and (d) be capable of dealing with theoretical constructs and measurement errors. The second problem relates to the scarcity of regional data, both concerning time-series and cross-sectional data. A linear structural equation model is presented which meets the first set of conditions. This model is then modified in such a way that it can be estimated by pooling cross-sectional and time-series data, thus increasing the number of observations. In addition, as an application the effects of regional industrialization policy on investments in the Netherlands during the period 1973–1976 are estimated.

scholarly journals Metode ARIMA untuk Memodelkan Volume Produksi Kelapa Sawit pada PT. SOCFINDO di Kabupaten Aceh Tamiang

2021 ◽  
Vol 3 (2) ◽  
pp. 309-319
Author(s):  
Wiwin Apriani ◽  
◽  
Rahmi Hayati
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Stationary State ◽  
Oil Palm ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
Selection Of

This study aims to create a mathematical model that can be used to predict the amount of oil palm that will be produced at PT. Socfindo in Aceh Tamiang Regency in the coming period. The data used is data on the amount of oil palm that is ready to be produced every month in 2012-2015. The method used is the ARIMA method. The selection of this method is based on the data used, namely time series data. Before carrying out further testing, first, ensure that the data used meets the stationary state. From the test results, it is found that the data used fulfills the stationary state, then it is found that the MA (1) model can be used to predict the time series data. Furthermore, we obtain a model that can be used to predict the volume of oil palm production at PT. Socfindo is: Z_t = a_t-0.4096a_ (t-1) +521.57 With a_t ~ N (0; 29192.72)

The declining influence of class and ideology in union membership: Consistent but divergent trends among Swedish employees

2017 ◽  
Vol 41 (2) ◽  
pp. 351-371 ◽  
Author(s):  
Johanna Palm
Keyword(s):  
Time Series ◽  
Empirical Analysis ◽  
Union Membership ◽  
Time Series Data ◽  
General Trend ◽  
Union Density ◽  
Series Data ◽  
Class Identity ◽  
Individual Level ◽  
Over Time

Using individual-level time-series data covering the period from 1990 to 2011, this article provides an empirical analysis of how the influence of various aspects of class and ideology on union organization have changed over time in the Swedish context. The primary results indicate that although union density and the influence of class-related aspects and ideology are decreasing, particularly among groups with traditionally high levels of organization, the general trend is not valid for all categories of employees. Rather, it appears that where the tradition of being organized is weaker, the influence of class and class identity is particularly strong. No evidence is identified that supports the thesis of class loyalty vanishing among the young.

Choosing the content of textual summaries of large time-series data sets

2006 ◽  
Vol 13 (1) ◽  
pp. 25-49 ◽  
Author(s):  
JIN YU ◽  
EHUD REITER ◽  
JIM HUNTER ◽  
CHRIS MELLISH
Keyword(s):  
Time Series ◽  
Gas Turbines ◽  
Time Series Data ◽  
Sensor Data ◽  
Series Data ◽  
Prototype System ◽  
Data Sets ◽  
Significant Patterns ◽  
Numeric Data ◽  
Selection Of

Natural Language Generation (NLG) can be used to generate textual summaries of numeric data sets. In this paper we develop an architecture for generating short (a few sentences) summaries of large (100KB or more) time-series data sets. The architecture integrates pattern recognition, pattern abstraction, selection of the most significant patterns, microplanning (especially aggregation), and realisation. We also describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textualsummaries of sensor data from gas turbines.

scholarly journals SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING

2020 ◽  
Vol 13 (2) ◽  
pp. 116-124
Author(s):  
Hermansah Hermansah ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Input Variables ◽  
Nonlinear Autoregressive ◽  
Selection Of ◽  
Autoregressive Neural Network

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.

scholarly journals PENGGEROMBOLAN DERET WAKTU DENGAN PENDEKATAN UKURAN KEMIRIPAN PICCOLO UNTUK PERAMALAN CURAH HUJAN PROVINSI BANTEN

2020 ◽  
Vol 4 (2) ◽  
pp. 382-391
Author(s):  
Sarah Fadhlia ◽  
I Made Sumertajaya ◽  
Anik Djuraidah
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Distance Method ◽  
Individual Level ◽  
Average Accuracy ◽  
Past Data ◽  
Distance Approach

Time series data modeling can be done by modeling each object one by one. Monthly rainfall data is an example of time series data. The purpose of time series analysis is to find patterns of past data and then forecast the future characteristics of data. The data used in this study is the Banten Province rainfall data which contained 19 rainfall stations. So it will require 19 models to forecast the rainfall data. The pattern of time series data in Banten Province monthly rainfall data in several locations has similarities. So that the similarity of this pattern can be considered in the clusters. In time series clustering, the idea is to investigate the similarity of time series in a cluster. The accuracy of distance similarity size measurements is performed on the generation data generated from 3 models, namely AR (1), AR (2), and AR (3). The piccolo method has an average accuracy of 0.62. While the maharaj method has an average accuracy of 0.41. This means that the Ward hierarchical clustering method using the Piccolo distance approach has a greater accuracy value than the Maharaj distance approach. Furthermore, the Piccolo method can be used as an alternative to the excellent distance method for grouping time series data in case data. The Banten Province rainfall station has 3 optimal clusters. Modeling individual level and cluster level has accuracy values that are not much different.

Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Akhbamah Primadaniyah Febrin ◽  
Itasia Dina Sulvianti ◽  
Aji Hamim Wigena
Keyword(s):  
Time Series ◽  
Structural Equation ◽  
Broiler Chicken ◽  
Time Series Data ◽  
Retail Price ◽  
Series Data ◽  
Percentage Error ◽  
Average Percentage ◽  
Independent Variables ◽  
Real Price

The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errors  combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.

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