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Thesis Milestones — Trailing Mean Series + Explicit Missing-Value Fill
x402
Thesis Milestones — Trailing Mean Series + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Computes trailing mean time series and fills missing values in thesis milestone records (completed and planned milestones) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Thesis milestones — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied thesis milestones records (completed_milestones, planned_milestones). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two analytical reports: (1) a trailing mean time series computed over the specified rolling window for the chosen milestone field, and (2) the dataset with missing values explicitly filled using the specified method (zero, mean, or median). Results cover all supplied rows up to 200.
Example use cases
Smooth PhD cohort milestone trends
“I have weekly thesis progress data for 3 PhD cohorts over the past year — please run a trailing mean with a window of 4 on the completed_milestones field and fill any gaps with the median so I can see smoothed completion trends.”
Impute missing planned milestones data
“My planned_milestones column has some nulls in my thesis tracking dataset of about 150 rows — can you fill those missing values using the mean method and also give me a trailing mean series with window size 2?”
Compare smoothed completion vs planning across groups
“I've got thesis milestone records for group-a and group-b with dates and both completed and planned milestone counts — run a trailing mean series with window 5 on completed_milestones and use zero-fill for any missing values so I can compare trends across the two groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-21-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have thesis or academic milestone tabular data (up to 200 rows) and need both rolling-window smoothing and missing-value imputation in a single low-cost call. It is ideal for education analytics workflows where you want offline, local computation without any external lookups or predictions — particularly when your data includes completed vs planned milestone columns grouped by course or cohort.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level errors
- Missing required fields (row_id, date) cause validation failure
- Window size larger than number of rows produces sparse or empty trailing means
- All values null in chosen field with mean/median fill method may produce NaN results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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