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1,118 results for “Time series”
Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can<br> say that Y=T regression is not so good.</p>
Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can<br> say that Y=T regression is totally fit.<br> This paper also comprises of comparative study of performance(mse) plot of both network.</p>
Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Methodology<br> This paper develops an ANN based comparative predictive model for NASDAQ stock<br> prediction. The first ANN model is developed with Multi-Layer Feed forward Network<br> Architecture & the second model is developed with Recurrent Neural Network Architecture. In this<br> paper gradient descent based back propagation learning algorithm is used for the supervised<br> learning of the predictive network.</p>
Bedload rate time series
<p><strong>Files R1qsraw.txt to R7qsraw.txt</strong>: Bedload rate (g/s) time series for total bedload (column 1) and 14 grain size fractions over seven experimental runs (R1 to R7). Fractional bedload rates go from column 2 to 15 as follows: <br> 0.5-0.7 mm (column 2)<br> 0.7-1 mm (column 3)<br> 1-1.4 mm (column 4)<br> 1.4-2 mm (column 5)<br> 2-2.8 mm (column 6)<br> 2.8-4 mm (column 7)<br> 4-5.6 mm (column 8)<br> 5.6-8 mm (column 9)<br> 8-11 mm (column 10)<br> 11-16 mm (column 11)<br> 16-22 mm (column 12)<br> 22-32 mm (column 13)<br> 32-45 mm (column 14)<br> 45-64 mm (column 15)</p> <p><strong>File SurfaceDg.txt</strong>: Contains the geometric mean size for the bed surface in mm (column 2) for different experimental times (time in h, column 1)</p> <p><strong>File BedloadDg.txt</strong>: Contains the geometric mean size for the bedload in mm (column 2) every 1 h (time in h, column 1)</p> <p><strong>File slope.txt: </strong>Contains the bed slope at the thalweg in m/m (column 2) for different experimental times (time in h, column 1)</p> <p> </p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)
<p>Note that the Tabulated value equals 3.841 while the Q value is less than Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 11. Drawing the time series for males and females primary stage after the First difference
<p>We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 9. Drawing the time series for males and females primary stage students
<p>The Unit Radix Dickey-Fuller Test is used to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.736458 , Statistic Test = 0.380545 , P-Value = 0.794 We notice from the values above P-Value = 0.794on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis ( The Nonexistence of a Radix Unit) implies that the time series is stable. Figure (9) represents the time series of females and males in the primary stage students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 5. Drawing the time series for primary stage female students
<p>We use the Unit Radix Dickey-Fuller Test to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.233403, Statistic Test = 0.125769, P-Value = 0.6405 The values above P-Value = 0.6405 is noted on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis (The Nonexistence of a Radix Unit) implies that the time series is stable. Figure 5 represents the Time series of Female Primary Stage Students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 3. Drawing the time series for Males primary stage students after getting the first difference
<p>The Unit Radix Dickey-Fuller Test is used to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.276151, Statistic Test = 0.87796, P-Value = 0.8984 We get to notice from the values above P-Value = 0.8984 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is observed that the stability of the Time Series has been accomplished . See figure 3.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 1. Drawing the time series for males primary stage
<p>After collecting all the students’ dropout proportion for both males and females in the<br> primary stage, the first step of the Box-Jenkins is to draw the time chain data to understand the<br> chain's attitude.</p>
Time series of detonation velocity for Fickett's model for various values of activation energy
<p>This dataset contains several time series of detonation velocity for Fickett's model.</p> <p>Parameters are: q=4, resolution per unit lenth is 1280.</p> <p>Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.</p>
Time series of Inland Surface Water Dataset in China (ISWDC)
<p>The Inland Surface Water Dataset in China (ISWDC) maps the water body larger than 0.0625 km<sup>2</sup> in the terrestrial land of China for the period 2000–2016, in 8-day temporal and 250 m spatial resolution. It is closely correlated with the national reference data with the determinant coefficients (R<sup>2</sup>) greater than 0.99 in 2000, 2005, and 2010, and possess very good consistency, very similar change dynamics, and similar spatial patterns in different regions with the GSW dataset. The ISWDC data set can be used for studies on the inter-annual and seasonal variation of the surface water systems. It can also be used as reference data for other surface water data set verification and as input parameter for regional and global hydro-climatic models.</p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
Validated onshore and offshore time series for European countries (1979-2017)
<p>This repository comprises hourly time series representing the onshore and offshore wind capacity factors in every European country (EU-28 except the islands Malta and Cyprus plus Norway and Switzerland) from 1979 to 2017. The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity. 3 letter codes (ISO-3166-3) are used to identify the countries.</p> <p>For every country, onshore wind time series are included. For some of the countries, offshore wind time series are also included. In both cases, the time series include data for the period 1979-2017. However, for every year, the installed capacity layout is kept fixed and corresponds to turbines running in 2015. By doing so the time series for different years represent the weather influenced on the wind generation and are not impacted by differences in installed capacities.</p> <p>To obtain onshore and offshore wind time series, wind velocity from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The methodology was described in detail and validated for Denmark in <a href="https://www.sciencedirect.com/science/article/pii/S0360544215012815">Andresen <em>et al</em>., Energy 93 (2015)</a>. In the text annexed to the data files, a description of the data and parameters used to bias-correct the modelled time series for every European country is provided.</p> <p>The license for the AU REatlas wind time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Validated onshore and offshore time series for European countries (1979-2017), RE-INVEST project (2019) </em></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>These time series were generated in the framework of <a href="https://reinvestproject.eu/">RE-INVEST project</a>. A similar dataset comprising solar photovoltaic time series at national scale can be accessed through the zenodo repository <a href="https://zenodo.org/record/2613651#.XPZ00mNS8uU">10.5281/zenodo.1321809</a> and details can be found in <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/pip.3126">Victoria and Andresen, Prog. In Phot.: Res and App. (2019) </a> </p> <p> </p> <p> </p>
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
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International Brain Laboratory public data
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OpenNeuro
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