scholarly journals Perubahan Demografi dan Pertumbuhan Ekonomi : Bukti Empiris Kasus Indonesia

2020 ◽  
Vol 5 (3) ◽  
pp. 161
Author(s):  
Paulina Harun
Keyword(s):  
Time Series ◽  
Model Data ◽  
Swan Model

Perubahan demografi yang terjadi di banyak negara berkembang berdampak pada seluruh komponen yang ada dalam ekonomi suatu negara, demikian juga hal ini terjadi di Indoensia. Perubahan demografi yang tidak terkendali tentunya akan berdampak pada kondisi ekonomi negara tersebut. Tujuan penelitian ini adalah untuk menganalisis sejauhmana perubahan demografi berdampak pada pertumbuhan ekonomi serta faktor-faktor yang mempengaruhinya.Penelitian ini adalah penelitian deskriptif kuantitatif yang mencoba menganalisa dampak perubahan demografi terhadap pertumbuhan ekonomi. Adapaun variabel yang dipergunakan adalah pertumbuhan ekonomi, pendapatan per kapita, pertumbuhan penduduk, rasio pekerja dengan jumlah penduduk, penduduk usia muda dan tua, serta harapan hidup masyarakat. Model analisis yang digunakan adalah reflikasi dari model Bloom Williamson, and Yu; Higgins dan Wiiliamson, dengan teori utama adalah Solow-Swan Model. Menggunakan data time series selama 19 tahun, dan data panel selama 9 tahun dengan 13 provinsi terpilih di Indonesia.Hasil penelitian menunjukkan, bahwa : untuk model regresi berganda, variabel angka harapan hidup serta rasio pekerja penduduk berpengaruh posistif signifikan terhadapa pertumbuhan ekonomi untuk keseluruhan model yang digunakan, sedangkan pertumbuhan populasi berpengaruh negatif terhadap pertumbuhan ekonomi. Sedangkan untuk model data panel per provinsi pertumbuhan pekerja, pendapatan perkapita, pertumbuhan populasi serta usia tua dan muda berpengaruh terhadap peertumbuhan ekonomi, namun rasio perkerja penduduk dan rasio pekerja berpengaruh negatif terhadap pertumbuhan ekonomi.

scholarly journals A hybrid wavelet analysis-cloud model data-extending approach for meteorologic and hydrologic time series

2015 ◽  
Vol 120 (9) ◽  
pp. 4057-4071 ◽  
Author(s):  
Dong Wang ◽  
Hao Ding ◽  
Vijay P. Singh ◽  
Xiaosan Shang ◽  
Dengfeng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Cloud Model ◽  
Model Data ◽  
Hydrologic Time Series

scholarly journals Neural Network Classifiers for Local Wind Prediction

2004 ◽  
Vol 43 (5) ◽  
pp. 727-738 ◽  
Author(s):  
Ralf Kretzschmar ◽  
Pierre Eckert ◽  
Daniel Cattani ◽  
Fritz Eggimann
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Performance Measure ◽  
Lead Times ◽  
Wind Gust ◽  
Model Data ◽  
The Neural Network ◽  
Neural Network Classifiers ◽  
Selection Of

Abstract This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

Sea Surface Height Predictions from the Global Navy Coastal Ocean Model during 1998–2001*

2004 ◽  
Vol 21 (12) ◽  
pp. 1876-1893 ◽  
Author(s):  
Charlie N. Barron ◽  
A. Birol Kara ◽  
Harley E. Hurlburt ◽  
C. Rowley ◽  
Lucy F. Smedstad
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Tide Gauge ◽  
Ocean Model ◽  
Sea Surface ◽  
Tide Gauges ◽  
Model Data ◽  
Free Running ◽  
R Value ◽  
Coastal Ocean Model

Abstract A ⅛° global version of the Navy Coastal Ocean Model (NCOM), operational at the Naval Oceanographic Office (NAVOCEANO), is used for prediction of sea surface height (SSH) on daily and monthly time scales during 1998–2001. Model simulations that use 3-hourly wind and thermal forcing obtained from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are performed with/without data assimilation to examine indirect/direct effects of atmospheric forcing in predicting SSH. Model–data evaluations are performed using the extensive database of daily averaged SSH values from tide gauges in the Atlantic, Pacific, and Indian Oceans obtained from the Joint Archive for Sea Level (JASL) center during 1998–2001. Model–data comparisons are based on observations from 282 tide gauge locations. An inverse barometer correction was applied to SSH time series from tide gauges for model–data comparisons, and a sensitivity study is undertaken to assess the impact of the inverse barometer correction on the SSH validation. A set of statistical metrics that includes conditional bias (Bcond), root-mean-square (rms) difference, correlation coefficient (R), and nondimensional skill score (SS) is used to evaluate the model performance. It is shown that global NCOM has skill in representing SSH even in a free-running simulation, with general improvement when SSH from satellite altimetry and sea surface temperature (SST) from satellite IR are assimilated via synthetic temperature and salinity profiles derived from climatological correlations. When the model was run from 1998 to 2001 with NOGAPS forcing, daily model SSH comparisons from 612 yearlong daily tide gauge time series gave a median rms difference of 5.98 cm (5.77 cm), an R value of 0.72 (0.76), and an SS value of 0.45 (0.51) for the ⅛° free-running (assimilative) NCOM. Similarly, error statistics based on the 30-day running averages of SSH time series for 591 yearlong daily tide gauge time series over the time frame 1998–2001 give a median rms difference of 3.63 cm (3.36 cm), an R value of 0.83 (0.85), and an SS value of 0.60 (0.64) for the ⅛° free-running (assimilated) NCOM. Model– data comparisons show that skill in 30-day running average SSH time series is as much as 30% higher than skill for daily SSH. Finally, SSH predictions from the free-running and assimilative ⅛° NCOM simulations are validated against sea level data from the tide gauges in two different ways: 1) using original detided sea level time series from tide gauges and 2) using the detided data with an inverse barometer correction derived using daily mean sea level pressure extracted from NOGAPS at each location. Based on comparisons with 612 yearlong daily tide gauge time series during 1998–2001, the inverse barometer correction lowered the median rms difference by about 1 cm (15%–20%). Results presented in this paper reveal that NCOM is able to predict SSH with reasonable accuracies, as evidenced by model simulations performed during 1998–2001. In an extension of the validation over broader ocean regions, the authors find good agreement in amplitude and distribution of SSH variability between NCOM and other operational model products.

scholarly journals FTIR time series of stratospheric NO2 over Hefei, China, and comparisons with OMI and GEOS-Chem model data

2019 ◽  
Vol 27 (16) ◽  
pp. A1225 ◽  
Author(s):  
Hao Yin ◽  
Youwen Sun ◽  
Cheng Liu ◽  
Lin Zhang ◽  
Xiao Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Model Data

Representing chemical history for ozone time-series predictions - a method development study for deep learning models

2021 ◽  
Author(s):  
Felix Kleinert ◽  
Lukas H. Leufen ◽  
Aurelia Lupascu ◽  
Tim Butler ◽  
Martin G. Schultz
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Method Development ◽  
Air Pollutant ◽  
Learning Models ◽  
Model Data ◽  
Spatial Coverage ◽  
Chemical History ◽  
Forecast Quality

<p>Machine learning techniques like deep learning gained enormous momentum in recent years. This was mainly caused by the success story of the main drivers like image and speech recognition, video prediction and autonomous driving, to name just a few.<br>Air pollutant forecasting models are an example, where earth system scientists start picking up deep learning models to enhance the forecast quality of time series. Almost all previous air pollution forecasts with machine learning rely solely on analysing temporal features in the observed time series of the target compound(s) and additional variables describing precursor concentrations and meteorological conditions. These studies, therefore, neglect the "chemical history" of air masses, i.e. the fact that air pollutant concentrations at a given observation site are a result of emission and sink processes, mixing and chemical transformations along the transport pathways of air.<br>This study develops a concept of how such factors can be represented in the recently published deep learning model IntelliO3. The concept is demonstrated with numerical model data from the WRF-Chem model because the gridded model data provides an internally consistent dataset with complete spatial coverage and no missing values.<br>Furthermore, using model data allows for attributing changes of the forecasting performance to specific conceptual aspects. For example, we use the 8 wind sectors (N, NE, E, SE, etc.) and circles with predefined radii around our target locations to aggregate meteorological and chemical data from the intersections. Afterwards, we feed this aggregated data into a deep neural network while using the ozone concentration of the central point's next timesteps as targets. By analysing the change of forecast quality when moving from 4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r, φ, t) sectors and thinning out the underlying model data, we can deliver first estimates of expected performance gains or losses when applying our concept to station based surface observations in future studies.</p>

RECURRENCE QUANTIFICATION ANALYSIS IN WATERSHED ECOSYSTEM RESEARCH

2011 ◽  
Vol 21 (04) ◽  
pp. 1113-1125 ◽  
Author(s):  
HOLGER LANGE
Keyword(s):  
Time Series ◽  
Recurrence Quantification Analysis ◽  
Data Driven ◽  
Model Data ◽  
Ecosystem Research ◽  
Data Comparison ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Term Monitoring

In ecosystem research, data-driven approaches to modeling are of major importance. Models are more often than not shaped by the spatiotemporal structure of the observations: an inverse modeling approach prevails. Here, I investigate the insights obtained from Recurrence Quantification Analysis of observed ecosystem time series. As a typical example of available long-term monitoring data, I choose time series from hydrology and hydrochemistry. Besides providing insights into the nonstationary and nonlinear dynamics of these variables, RQA also enables a detailed and temporally local model-data comparison.

scholarly journals Analisis Pengaruh Faktor-Faktor Fundamental terhadap Harga Saham pada Bank - Bank Plat Merah tahun 2008-2018

2020 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Firnanda Novita R ◽  
Maureen Inesella LT ◽  
David Kaluge
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Model Data ◽  
Data Pooling ◽  
Book Value ◽  
Equity Ratio

Abstrak :Pasar modal sebagai sarana investor dalam menjual dan membeli saham (stock) dan obligasi (bond), dimana perusahaan itu memiliki tujuan bahwa hasil penjualan saham itu bisa dimanfaatkan untuk memperkuat dana perusahaan. Penilaian harga saham secara akurat bisa meminimalkan resiko sekaligus membantu investor mendapatkan keuntungan, mengingat investasi saham di pasar modal merupakan jenis investasi yang beresiko tinggi meskipun menjanjikan keuntungan relatif besar.Faktor-faktor fundamental yang diwakili oleh kinerja keuangan menjadi sumber informasi sebagai bahan pertimbangan dalam keputusan investor.Tujuan penelitian ini adalah untuk mengetahui bagaimana pengaruh faktor-faktor fundamental secara parsial dan simultan terhadap harga saham pada Bank - Bank Plat Merah pada tahun 2008-2018.Variabel yang digunakan dalam penelitian ini adalah faktor-faktor fundamental yang diwakili kinerja keuangan sebagai variabel independen dan harga saham sebagai variabel dependen. Kinerja keuangan diukur dengan Earning per Share (X1), Price Earning Ratio (X2), Price to Book Value (X3), Return to Equity (X4) dan Debt to Equity Ratio (X5) serta harga saham (Y). Metode penelitian yangdigunakan adalah model data panel. Data yang digunakan dalam penelitian ini adalah data pooling, yang merupakan kombinasi antara data cross section dan data time series yang diambil dari laporan keuangan tahunan bank-bank plat merah yaitu Bank Mandiri, Bank BRI, Bank BNI dan Bank BTN serta data harga saham yang diambil dari Bursa Efek Indonesia. Pengujian data dilakukan dengan menggunakan analisis statistik yaitu analisis data panel, uji t dan uji F. Uji t digunakan untuk mengguji pengaruh variabel independen secara parsial terhadap variabel dependen. Uji F digunakan untuk menguji pengaruh variabel independen secara bersama-sama terhadap variabel dependen.Hasil penelitian menunjukkan bahwa variabel independen, faktor-faktor fundamental yang diwakili kinerja keuangan secara bersama-sama berpengaruh signifikan terhadap harga saham. Faktor - faktor fundamental yang diwakili oleh variabel EPS, PBV, ROE, dan DER berpengaruh signifikan secara parsial terhadap harga saham, sedangkan variabel PER tidak berpengaruh signifikan secara parsial terhadap harga saham pada Bank-Bank Plat Merah pada tahun 2008-2018.  Kata kunci :Faktor-Faktor Fundamental, Kinerja Keuangan, Harga Saham

scholarly journals Model Data Mining Time Series Untuk Prediksi Profitabilitas Perusahaan

2020 ◽  
Vol 3 (1) ◽  
pp. 8
Author(s):  
Muksan Junaidi ◽  
Adhika Pramita Widyassari
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Input ◽  
Model Data ◽  
Neuro Fuzzy

<strong><em>Profitabilitas merupakan rasio keuangan yang memperlihatkan kemampuan operasional perusahaan untuk menghasilkan laba operasi melalui modal sendiri. Untuk melihat kondisi kemampuan operasional perusahaan pada masa mendatang, bisa dilakukan dengan memprediksi nilai rasio tersebut. Teknik dalam menangani masalah prediksi ini adalah menggunakan model data mining. Penelitian ini bertujuan mengembangkan aplikasi prediksi profitabilitas keuangan perusahaan melalui model data mining neuro-fuzzy ANFIS dengan data time series. Data penelitian adalah data sekunder bersifat kuantitatif dari wibe site www.idx.co.id. Populasi data dari perusahaan emiten LQ45 di Bursa Efek  Indonesia(BEI) tahun 2011-2016 berjumlah 45 perusahaan. Pemrosesan awal menghitung nilai rasio keuangan perusahaan yang akan dipakai sebagai data input pada model ANFIS. Simulasi dan evaluasi model menggunakan aplikasi GUI program Matlab. Perbandingan antara perhitungan rasio profitabilitas tahun yang di prediksi dengan nilai dari model ANFIS. Hasil akhir menunjukkan bahwa nilai model ANFIS sangat optimal, efisien, konsisten dan paling mendekati rata-rata nilai rasio tahun yang di prediksi sebesar 9.95% pada fungsi keanggotaan Segitiga. Sedang hasil prediksi tiga fungsi keanggotaan lainnya Trapesium, G-bell dan Gauss kurang optimal.</em></strong>

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