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8 results for “Window Size”
Data from: Evaluating Window Size Effects on Univariate Time Series Forecasting with Machine Learning
<p>In the realm of time series prediction modeling, the window size (w) is a critical hyperparameter that determines the number of time units included in each example provided to a learning model. This hyperparameter is crucial because it allows the learning model to recognize both long-term and short-term trends, as well as seasonal patterns, while reducing sensitivity to random noise. This study aims to elucidate the impact of window size on the performance of machine learning algorithms in univariate time series forecasting tasks. To achieve this, we employed 40 time series from two different domains, conducting experiments with varying window sizes using four types of machine learning algorithms: Bagging, Boosting, Stacking, and a Recurrent Neural Network (RNN) architecture. The results reveal that increasing the window size generally enhances the evaluation metric values up to a stabilization point, beyond which further increases do not significantly improve predictive accuracy. This stabilization effect was observed in both domains when w values exceeded 100 time steps. Moreover, the study found that RNN architectures do not consistently outperform ensemble models in various univariate time series forecasting scenarios.</p>
Wembedder wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20
<p>Wikidata embedding<br> ==================</p> <p>Gensim model:<br> wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20</p> <p>Download of Wikidata from::</p> <p> https://dumps.wikimedia.org/wikidatawiki/entities/</p> <p>Trigram construction::</p> <p> from bz2 import BZ2File<br> import re</p> <p> dump_filename = 'wikidata-20170613-truthy-BETA.nt.bz2'<br> trigram_filename = 'wikidata-20170613-truthy-BETA.trigrams'</p> <p> pattern = re.compile(<br> (r'^<http://www.wikidata.org/entity/(Q\d+)> '<br> r'<http://www.wikidata.org/prop/direct/(P\d+)> '<br> r'<http://www.wikidata.org/entity/(Q\d+)>'),<br> flags=re.UNICODE)</p> <p> with open(trigram_filename, 'w') as f:<br> for line in BZ2File(dump_filename):<br> line = line.decode('utf-8')<br> match = pattern.search(line)<br> if match:<br> f.write(" ".join(match.groups()) + '\n')</p> <p><br> Construction of Gensim model::<br> <br> import logging<br> from gensim.models import Word2Vec<br> from gensim.models.word2vec import LineSentence</p> <p> logging.basicConfig(<br> format='%(asctime)s : %(levelname)s : %(message)s',<br> level=logging.INFO)</p> <p> sentences = LineSentence('wikidata-20170613-truthy-BETA.trigrams')</p> <p> filename = 'wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20'<br> w2v = Word2Vec(sentences, size=100, window=1, min_count=20, workers=10)<br> w2v.save(filename)</p> <p> </p>
Selecting a window size for the analysis of whole genome alignments using AIC
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FIGURE. DnaSP DNA polymorphism analysis - Nucleotide variability (Pi) comparison between Strobilanthes lupulina and S. glandulata. The window length and step size were set to 600bp and 200bp respectively. The most varying regions and the commonly used barcoding regions are listed against the base-pair differences found. in Strobilanthes glandulata (Acanthaceae), a new species from Sri Lanka based on the morphological and molecular evidences
FIGURE. DnaSP DNA polymorphism analysis - Nucleotide variability (Pi) comparison between Strobilanthes lupulina and S. glandulata. The window length and step size were set to 600bp and 200bp respectively. The most varying regions and the commonly used barcoding regions are listed against the base-pair differences found.
Wembedder wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20-iter=25
<p>Wikidata embedding<br> ==================</p> <p>Gensim model:<br> wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20-iter=25</p> <p>Download of Wikidata from::</p> <p> https://dumps.wikimedia.org/wikidatawiki/entities/</p> <p>Trigram construction::</p> <p> from bz2 import BZ2File<br> import re</p> <p> dump_filename = 'wikidata-20170613-truthy-BETA.nt.bz2'<br> trigram_filename = 'wikidata-20170613-truthy-BETA.trigrams'</p> <p> pattern = re.compile(<br> (r'^<http://www.wikidata.org/entity/(Q\d+)> '<br> r'<http://www.wikidata.org/prop/direct/(P\d+)> '<br> r'<http://www.wikidata.org/entity/(Q\d+)>'),<br> flags=re.UNICODE)</p> <p> with open(trigram_filename, 'w') as f:<br> for line in BZ2File(dump_filename):<br> line = line.decode('utf-8')<br> match = pattern.search(line)<br> if match:<br> f.write(" ".join(match.groups()) + '\n')</p> <p><br> Construction of Gensim model::<br> <br> import logging<br> from gensim.models import Word2Vec<br> from gensim.models.word2vec import LineSentence</p> <p> logging.basicConfig(<br> format='%(asctime)s : %(levelname)s : %(message)s',<br> level=logging.INFO)</p> <p> sentences = LineSentence('wikidata-20170613-truthy-BETA.trigrams')</p> <p> filename = 'wikidata-20170613-truthy-BETA-cbow-size=100-window=1-min_count=20-iter=25'<br> w2v = Word2Vec(sentences, size=100, window=1, min_count=20, workers=10, iter=25)<br> w2v.save(filename)</p> <p> </p>
Data from: Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour
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Testosterone exposure during fetal masculinization programming window determines the kidney size in adult mice
GEO Series GSE280108. Mus musculus. 19 samples. Type: Expression profiling by high throughput sequencing.
CT Window Setting to Select Double-lumen Tube Size
ClinicalTrials.gov study NCT01818102. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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