How can seasonality be analyzed per row? on a massive dataset with many observations? My code below does not work for large datasets (just on smaller ones). # Function to detect seasonal patterns using ACF for a single row detect_seasonal_acf <- function(row) { acf_result <- acf(row, lag.max = 12) return(acf_result$acf) } # Apply the function row-wise seasonal_acf_results <- apply(data[, -1], 1, detect_seasonal_acf) # Check for significant peaks (e.g., peaks above a certain threshold) significant_peaks <- sapply(seasonal_acf_results, function(acf_values) { max(acf_values) > 2/sqrt(length(acf_values)) }) # The 'significant_peaks' vector will indicate if there's a seasonal pattern for each keyword subset_data$Seasonal_Pattern_ACF <- significant_peaks
How can seasonality be analyzed per row? on a massive dataset with many observations? My code below does not work for large datasets (just on smaller ones). # Function to detect seasonal patterns using ACF for a single row detect_seasonal_acf <- function(row) { acf_result <- acf(row, lag.max = 12) return(acf_result$acf) } # Apply the function row-wise seasonal_acf_results <- apply(data[, -1], 1, detect_seasonal_acf) # Check for significant peaks (e.g., peaks above a certain threshold) significant_peaks <- sapply(seasonal_acf_results, function(acf_values) { max(acf_values) > 2/sqrt(length(acf_values)) }) # The 'significant_peaks' vector will indicate if there's a seasonal pattern for each keyword subset_data$Seasonal_Pattern_ACF <- significant_peaks
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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How can seasonality be analyzed per row? on a massive dataset with many observations?
My code below does not work for large datasets (just on smaller ones).
# Function to detect seasonal patterns using ACF for a single row
detect_seasonal_acf <- function(row) { acf_result <- acf(row, lag.max = 12)
return(acf_result$acf) }
# Apply the function row-wise
seasonal_acf_results <- apply(data[, -1], 1, detect_seasonal_acf)
# Check for significant peaks (e.g., peaks above a certain threshold)
significant_peaks <- sapply(seasonal_acf_results, function(acf_values) { max(acf_values) > 2/sqrt(length(acf_values)) })
# The 'significant_peaks'
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