News Article Based Industry Risk Index Prediction for Industry-Specific Evaluation

2021 ◽  
Author(s):  
Kyungwon Kim ◽  
Kyoungro Yoon
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Evaluation Method ◽  
Time Series Data ◽  
Risk Index ◽  
News Article ◽  
Series Data ◽  
Specific Interest ◽  
Analysis Model ◽  
Long Time

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Analisis Intervesi Fungsi Step Efek Program Tol-Laut Terhadap Pergerakan Harga Saham TMAS.JK

2019 ◽  
Vol 5 (01) ◽  
pp. 47-54
Author(s):  
Wigid Hariadi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Vice President ◽  
Step Function ◽  
Series Data ◽  
Intervention Analysis ◽  
Analysis Model ◽  
External Events ◽  
Port Construction

Abstract. Intervention analysis is used to evaluate the effect of external events on a time series data. Sea-highway program is one of the leading programs Joko Widodo-Jusuf Kalla in presidential election 2014. So the author want to modeling the effect from Sea-highway programs on stock price movement in the shipping sector, TMAS.JK (Pelayaran Tempuran Emas tbk). After analyzing, proven that it has happened intervention on movement of daily stock price TMAS.JK caused by Sea-highway programs. Intervention I, on 11 August 2014, which was efect as a result of the election of the Joko Widodo-Jusuf kalla pair as President and vice President Republic of Indonesia on 22 july 2014. Intervention II, on 10 november 2014, president Joko Widodo speech in APEC about Sea-highway Program, and offering investment in port construction to foreign country. So that the model of time series analysis that right is intervention analysis model multi input step function, where the model is ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1).  Keywords: Intervention Analysis, Multi Input, Step Function, Sea-highway.    Abstrak. Analisis intervensi digunakan untuk mengevaluasi efek dari peristiwa eksternal pada suatu data time series. Program Tol-Laut merupakan salah satu program unggulan pasangan Joko Widodo-Jusuf Kalla dalam pemilu 2014. sehingga, penulis ingin memodelkan efek dari Program Tol-Laut terhadap pergerakan harga saham dibidang pelayaran, TMAS.JK (Pelayaran Tempuran Emas tbk). Setelah dilakukan analisis data, terbukti bahwa terjadi intervensi pada pergerakan harga saham harian TMAS.JK yang disebabkan oleh efek dari program Tol-Laut. Dimana intervensi I, pada tanggal 11 Agustus 2014, yang diduga sebagai dampak dari terpilihnya pasangan Joko widodo-Jusuf Kalla sebagai presiden dan wakil presiden Republik Indonesia pada tanggal 22 Juli 2014. Intervensi II, pada tanggal 10 November 2014, pidato Presiden Joko Widodo di forum APEC mengenai program  tol  laut, dan  menawarkan investasi dibidang pembangunan pelabuhan  kepada bangsa asing. Sehingga model analisis time series yang tepat adalah model analisis intervensi multi input fungsi step, dimana modelnya adalah ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1). Kata kunci: Analisis intervensi, Multi Input, fungsi step, Tol-Laut.

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

scholarly journals A hybrid elman neural network predictor for time series prediction

2018 ◽  
Vol 7 (2.20) ◽  
pp. 159 ◽  
Author(s):  
N Mohana Sundaram ◽  
S N. Sivanandam
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Delay ◽  
Adaptive Learning ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Elman Neural Network ◽  
Dynamic Processing ◽  
Elman Recurrent Neural Network

Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.  

scholarly journals Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

2020 ◽  
Vol 245 ◽  
pp. 07001
Author(s):  
Laura Sargsyan ◽  
Filipe Martins
Keyword(s):  
Time Series ◽  
Control System ◽  
Distribution System ◽  
Large Scale ◽  
High Energy Physics ◽  
Time Series Data ◽  
High Energy ◽  
Series Data ◽  
Test Version ◽  
Long Time

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

scholarly journals A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4466
Author(s):  
Li Guo ◽  
Runze Li ◽  
Bin Jiang
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Fault Prediction ◽  
Data Driven ◽  
Series Data ◽  
Sequence Prediction ◽  
Long Time Series ◽  
Long Time

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN TENAGA LISTRIK KONSUMEN SEKTOR KONSUMTIF PERUSAHAAN LISTRIK NEGARA

2017 ◽  
Vol 14 (3) ◽  
pp. 330
Author(s):  
Aminullah Assagaf
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Negative Impact ◽  
Data Distribution ◽  
Electricity Demand ◽  
Series Data ◽  
Analysis Model ◽  
Demand And Supply ◽  
Independent Variable

This research aims to analyse electricity demand, and focus for consumptive sector in PT Perusahaan listrik Negara (Persero) or PT PLN (Persero). While selected by consumptive sector is some region in Jawa Bali and otuside Jawa Bali. Step of research and process result based on SPSS calculation, and use time series data year 1995 - 2009. As for used analysis model follow its data distribution that is the non linear regression model being based on Ln with dependent variable is demand electricity or kWh sales, and independent variable consist of install capacity, average tariff, and rate of capacity using percustomers. Obtain result that install capacity and rate of capacity using percustomers have given positif impact, and average tariff have given negative impact. All of that independent variable have significant influence, and install capacity variable most its influence significant to electricity demand of consumptive sector. PLN’s management has to observe growth of explanatory variable to make policy for demand and supply equilibrium and toward customers satisfaction.

scholarly journals Observing the Ocean

Eos ◽  
2017 ◽  
Vol 98 ◽  
Author(s):  
Toste Tanhua
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Long Time Series ◽  
Long Time

How measurements from a glider deployed off the coast of Peru are contributing to a much-needed long time-series data set.

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