[amp_mcq option1=”Simple random sampling of time series is probably the best way to resample times series data.” option2=”Three parameters are used for time series splitting” option3=”Horizon parameter is the number of consecutive values in test set sample” option4=”All of the mentioned” correct=”option1″]<\/p>\n
The correct answer is: A. Simple random sampling of time series is probably the best way to resample times series data.<\/strong><\/p>\n Simple random sampling is a sampling method in which each member of the population has an equal chance of being selected. This method is often used when the population is large and the researcher does not have a list of all the members of the population.<\/p>\n However, simple random sampling is not the best way to resample time series data. This is because time series data is often correlated, meaning that the values of one time series observation are related to the values of other time series observations. When you resample time series data, you want to make sure that the resampled data is still correlated. Simple random sampling does not guarantee that the resampled data will be correlated.<\/p>\n A better way to resample time series data is to use a method that takes into account the correlation between the time series observations. One such method is stratified sampling. Stratified sampling is a sampling method in which the population is divided into groups (strata) and then a random sample is selected from each stratum. This method ensures that the resampled data is representative of the population and that the data is still correlated.<\/p>\n Another way to resample time series data is to use a method that takes into account the time series structure. One such method is time series cross-validation. Time series cross-validation is a method in which the time series data is divided into multiple subsets. The model is then trained on one subset and evaluated on the other subsets. This method ensures that the model is not overfitting the data and that the model is still able to generalize to new data.<\/p>\n In conclusion, simple random sampling is not the best way to resample time series data. A better way to resample time series data is to use a method that takes into account the correlation between the time series observations or the time series structure.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”Simple random sampling of time series is probably the best way to resample times series data.” option2=”Three parameters are used for time series splitting” option3=”Horizon parameter is the number of consecutive values in test set sample” option4=”All of the mentioned” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[736],"tags":[],"class_list":["post-51485","post","type-post","status-publish","format-standard","hentry","category-machine-learning-algorithms","no-featured-image-padding"],"yoast_head":"\n