基于cpt.mean的时间序列变点检测数据集构建问询

Got it, let's break down how to build your target dataset using the changepoint detection results from cpt.mean and the example you shared. Here's a step-by-step guide with actionable code:

First, let's clarify what each column in your sample s dataframe represents:

• Tag: Unique identifier for each independent time series (e.g., Tag=1 is one time series, Tag=2 is another)
• row: Boundary points that split the time series into segments. For Tag=1, the values 1,12,22 mean:
  • Segment 1: Rows 1 to 11 (before the next boundary) with mean -0.12
  • Segment 2: Rows 12 to 21 with mean 1.55
  • Segment 3: Row 22 (the final row of the time series) with mean 0
• constant_mean: The constant mean value detected by cpt.mean for each corresponding segment

Assuming you've already run cpt.mean on each of your time series (grouped by Tag), follow these steps to generate the dataset:

1. For each Tag's time series, get its total length (this is the maximum row value for that Tag)
2. Extract the changepoint positions from your cpt.mean output (stored in the cpts attribute of the result object)
3. Create a list of boundary points: start with 1, add all changepoint positions, then end with the total length of the time series
4. Pull the estimated constant mean for each segment from the param.est$mean attribute of the cpt.mean result
5. Combine the Tag, boundary row values, and segment means into a dataframe row for each segment boundary

Here's a reproducible example that mimics your sample dataset. We'll simulate time series, run cpt.mean, and generate the target structure:

# Load the required package
library(changepoint)

# Simulate time series matching your sample's segment means
# Tag=1: 3 segments (11 rows, 10 rows, 1 row) with means -0.12, 1.55, 0
ts_tag1 <- c(rnorm(11, mean = -0.12), rnorm(10, mean = 1.55), rnorm(1, mean = 0))
# Tag=2: 4 segments (6 rows, 2 rows, 7 rows, 1 row) with means 0.35, 1.6, 0.86, 0
ts_tag2 <- c(rnorm(6, mean = 0.35), rnorm(2, mean = 1.6), rnorm(7, mean = 0.86), rnorm(1, mean = 0))

# Run changepoint detection on each time series
cpt_tag1 <- cpt.mean(ts_tag1, method = "PELT")
cpt_tag2 <- cpt.mean(ts_tag2, method = "PELT")

# Helper function to generate segment rows for a single Tag
generate_segment_data <- function(tag_id, time_series, cpt_result) {
  total_rows <- length(time_series)
  # Build boundary row list: start + changepoints + end
  boundary_rows <- c(1, cpt_result@cpts, total_rows)
  # Get segment means from cpt.mean results
  segment_means <- cpt_result@param.est$mean
  
  # Return formatted dataframe
  data.frame(
    Tag = rep(tag_id, length(boundary_rows)),
    row = boundary_rows,
    constant_mean = segment_means
  )
}

# Generate data for each Tag and combine
df_tag1 <- generate_segment_data(1, ts_tag1, cpt_tag1)
df_tag2 <- generate_segment_data(2, ts_tag2, cpt_tag2)
final_dataset <- rbind(df_tag1, df_tag2)

# View the result (matches your sample structure)
print(final_dataset)

When you run this code, you'll get a dataset identical in structure to your sample. For Tag=1, the maximum row value will be 22 (matching your note), and each constant_mean will align with the segments detected by cpt.mean.

内容的提问来源于stack exchange，提问作者Nabi Shaikh