All Services

Deduplicates rows and generates a base-100 indexed series from time series completeness data (observed vs. expected data points) for up to 200 rows.

Runs two local analytical reports on supplied time series completeness records: exact row deduplication and duplicate key detection, for up to 200 rows.

Deduplicates rows and computes an equal-width histogram over observed_points or expected_points fields in time series completeness data (up to 200 rows, two analytical reports per call).

Deduplicates exact rows and fills missing values (zero, mean, or median) in time series completeness records containing observed and expected point counts, returning 2 analytical reports for up to 200 rows.

Analyzes up to 200 time series completeness records (observed_points, expected_points) by deduplicating exact duplicate rows and flagging IQR-based outliers, producing two independent analytical reports.

Runs two local analytical reports on time series completeness data: exact row deduplication and numeric distribution analysis for observed_points or expected_points, across up to 200 rows.

Deduplicates exact rows and filters by numeric interval in time series completeness records (observed_points, expected_points) for up to 200 rows, producing 2 analytical reports.

Deduplicates and numerically orders time series completeness records (observed_points, expected_points) for up to 200 rows across 2 independent reports.

Analyzes time series completeness records by performing exact row deduplication and computing a trailing mean series for observed vs. expected data points, returning two analytical reports for up to 200 rows.

Analyzes time series completeness records (observed vs expected data points) to produce grouped numeric summaries and adjacent row change reports for up to 200 rows.

Analyzes time series completeness records by computing grouped numeric summaries and category frequency distributions for observed_points and expected_points across dataset groups.

Analyzes time series completeness records (observed vs expected data points) by producing two local analytical reports: a grouped numeric summary and a date-by-category pivot, for up to 200 rows.

Computes grouped numeric summaries and fills explicit missing values for time series completeness records (observed_points vs expected_points) across up to 200 rows in two independent reports.

Analyzes time series completeness data (observed vs expected points) grouped by dataset category, producing numeric summary statistics and IQR-based outlier flags across up to 200 rows.

Analyzes time series completeness records by producing a grouped numeric summary and a keyed dataset comparison report for observed vs. expected data points across up to 200 rows.

Analyzes time series completeness records by producing a grouped numeric summary and a numeric row ordering report for up to 200 rows of observed vs. expected data points.

Analyzes time series completeness data (observed vs expected points) by computing grouped numeric summaries and trailing mean series across up to 200 rows, producing two independent analytical reports.

Analyzes supplied time series completeness records (observed_points, expected_points) by producing a grouped numeric summary and a two-measure correlation report for up to 200 rows.

Analyzes time series completeness records (observed vs expected data points) using IQR-based outlier detection and keyed dataset comparison, producing two independent analytical reports for up to 200 rows.

Analyzes time series completeness records (observed vs expected data points) using IQR-based outlier detection and secondary-measure weighted mean, delivering two analytical reports for up to 200 rows.

Analyzes up to 200 rows of time series completeness data (observed vs expected points) using IQR-based outlier detection and two-measure correlation, returning two independent analytical reports.

Computes numeric distribution statistics and adjacent row-to-row change analysis for time series completeness records (observed vs. expected data points) across up to 200 rows.

Analyzes time series completeness records (observed vs expected data points) by generating both a numeric distribution report and a base-100 rebase series for up to 200 rows.

Analyzes up to 200 time series completeness records (observed vs expected data points) and returns two local analytical reports: a numeric distribution and category frequency breakdown.

Analyzes time series completeness records (observed vs expected data points) to produce a numeric distribution report and a daily sum series report for up to 200 rows.

Generates two local analytical reports (numeric distribution and date-by-category pivot) for time series completeness data (observed_points, expected_points) across up to 200 rows.

Generates a numeric distribution report and equal-width histogram for time series completeness data (observed_points vs expected_points) across up to 200 rows.

Computes a numeric distribution report and fills explicit missing values (zero, mean, or median) for up to 200 time series completeness records with observed and expected point counts.

Analyzes time series completeness records (observed_points vs expected_points) by running two independent analytical reports: a numeric distribution analysis and a keyed dataset comparison, for up to 200 rows.

Analyzes time series completeness records (observed vs expected points) by computing numeric distribution statistics and filtering by a numeric interval, returning two analytical reports for up to 200 rows.

Analyzes time series completeness records (observed vs expected data points) by generating both a numeric distribution report and a numeric row ordering report for up to 200 rows.

Computes numeric distribution statistics and a secondary-measure weighted mean for time series completeness records (observed_points vs expected_points), producing two analytical reports for up to 200 rows.

Computes a numeric distribution report and a trailing mean series for time series completeness records (observed_points vs expected_points) across up to 200 rows.

Filters time series completeness records by a numeric interval and generates a Base-100 indexed series for up to 200 rows of observed vs. expected data points, producing two independent analytical reports.

Filters time series completeness records by a numeric interval and generates a daily sum series from observed/expected point data, returning two analytical reports for up to 200 rows.

Filters time series completeness records by a numeric interval and computes an equal-width histogram for either observed_points or expected_points, returning two analytical reports for up to 200 rows.

Filters time series completeness records by a numeric interval and fills missing values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.

Analyzes time series completeness records by applying numeric interval filtering and comparing two keyed datasets (observed vs expected data points) across up to 200 rows, producing two independent analytical reports.

Analyzes time series completeness data (observed vs expected points) by applying a numeric interval filter and computing a trailing mean series, returning two independent analytical reports for up to 200 rows.

Filters time series completeness records by a numeric interval and computes correlation between observed_points and expected_points across up to 200 rows, producing two analytical reports.

Analyzes time series completeness data (observed vs expected points) to produce two reports: numeric row ordering and adjacent row changes, for up to 200 rows.

Computes numeric row ordering and base-100 indexed series for up to 200 time series completeness records (observed vs expected points), returning two independent analytical reports.

Generates two local analytical reports (numeric row ordering and equal-width histogram) for time series completeness data (observed vs. expected points) across up to 200 rows.

Orders time series completeness records numerically by row and fills missing observed_points or expected_points values using zero, mean, or median imputation, producing 2 analytical reports for up to 200 rows.

Analyzes time series completeness records by performing numeric row ordering and keyed dataset comparison across up to 200 rows, producing two independent analytical reports.

Analyzes time series completeness records (observed vs expected points) using numeric row ordering and interval filtering, producing 2 analytical reports for up to 200 rows.

Computes numeric row ordering and trailing mean series analytics for time series completeness records (observed vs expected data points) over up to 200 rows.

Audits time series completeness records for data quality issues and computes adjacent row changes across up to 200 rows, delivering two independent analytical reports per call.

Runs two analytical reports on supplied time series completeness records: a record quality audit and a category frequency breakdown, for up to 200 rows.

Runs two local analytical reports (record quality audit and daily sum series) on up to 200 time series completeness records containing observed and expected data points.

Performs a record quality audit and duplicate key detection report on up to 200 supplied time series completeness rows (observed_points, expected_points), returning two independent analytical reports.

Runs two local analytical reports (record quality audit and equal-width histogram) on up to 200 time series completeness rows containing observed and expected data point counts.

Audits up to 200 time series completeness records (observed_points vs expected_points) for data quality issues and exact duplicate rows, returning two independent analytical reports.

Runs three analytical reports on time series completeness data: a record quality audit, exact row deduplication, and a base-100 indexed series — all from supplied rows, up to 200 records.

Runs three local analytical reports on time series completeness records: a record quality audit, exact row deduplication, and a daily sum series, for up to 200 rows.

Audits up to 200 time series completeness records for quality issues, removes exact duplicate rows, and generates a duplicate key report — three analytical reports in one call.

Runs three analytics reports on time series completeness data (observed vs expected points): record quality audit, exact row deduplication, and equal-width histogram — all on supplied rows, up to 200, with no external lookups.

Runs three local analytical reports on time series completeness data (observed vs expected points): record quality audit, exact row deduplication, and IQR-based outlier flagging — up to 200 rows per call.

Runs three local analytical reports on time series completeness records: a record quality audit, exact row deduplication, and numeric distribution analysis for observed_points and expected_points fields.

Audits time series completeness records (observed_points vs expected_points) for data quality, removes exact duplicate rows, and filters rows by a numeric interval — returning 3 independent analytical reports for up to 200 rows.

Runs three independent analytical reports (record quality audit, exact row deduplication, and numeric row ordering) on time series completeness data containing observed and expected point counts, for up to 200 rows.

Runs three local analytical reports on time series completeness data: a record quality audit, exact row deduplication, and a trailing mean series calculation.

Audits data quality and fills missing values in time series completeness records (observed_points, expected_points) for up to 200 rows, producing two independent analytical reports.

Runs two analytical reports on supplied time series completeness data (observed vs expected points): a record quality audit and a grouped numeric summary, for up to 200 rows.

Runs two local analytical reports on supplied time series completeness data: a record quality audit and a keyed dataset comparison between two datasets, each with observed vs. expected data points.

Runs two analytical reports (record quality audit and numeric distribution) on time series completeness data (observed_points vs expected_points) for up to 200 rows, using local analytical engines with no external lookups.

Audits time series completeness records for quality issues and filters rows by a numeric interval across observed_points or expected_points, returning two independent analytical reports for up to 200 rows.

Runs three local analytical reports on time series completeness data: a record quality audit, string normalisation, and a base-100 indexed series — for up to 200 rows of observed vs expected data points.

Runs three local analytical reports (record quality audit, string normalisation, and daily sum series) on time series completeness data (observed vs expected points) for up to 200 rows.

Runs three local analytical reports on time series completeness data (observed vs expected points): record quality audit, string normalisation, and date-by-category pivot — for up to 200 rows.

Audits up to 200 time series completeness records for data quality issues, normalises strings, and identifies duplicate keys across three independent analytical reports.

Audits, normalises, and fills missing values in time series completeness records (observed_points vs expected_points) across up to 200 rows, producing 3 independent analytical reports.

Audits time series completeness records (observed_points vs expected_points) for data quality issues, normalises strings, and flags IQR-based outliers across up to 200 rows in three independent reports.

Audits time series completeness records for quality, normalises strings, and compares two keyed datasets, producing 3 independent analytical reports for up to 200 rows.

Runs three analytical reports (record quality audit, string normalisation, and numeric distribution) on supplied time series completeness records containing observed and expected data points, for up to 200 rows.

Analyzes time series completeness records (observed vs expected points) and produces two reports: a record quality audit and a trailing mean series, for up to 200 rows.

Computes a secondary-measure weighted mean and a date-by-category pivot table for up to 200 time series completeness records (observed_points vs expected_points) in a single call.

Computes string-normalised and base-100 indexed analytics for time series completeness data (observed vs expected points) across up to 200 rows, producing two independent reports.

Analyzes time series completeness records (observed vs expected data points) by running two local analytical reports: string normalisation and category frequency analysis, on up to 200 rows.

Performs string normalisation and IQR-based outlier detection on up to 200 rows of time series completeness records (observed_points vs expected_points), returning two independent analytical reports.

Computes a trailing mean series and fills explicit missing values for time series completeness records (observed vs expected points) for up to 200 rows in two combined reports.

Computes a trailing mean series and a two-measure Pearson correlation for time series completeness records (observed_points vs expected_points) across up to 200 rows.

Runs two analytical reports on time series completeness data: a Pearson/correlation analysis between observed_points and expected_points, and a date-by-category pivot table, for up to 200 rows.

Computes Pearson correlation between observed_points and expected_points in a time series and fills missing values using zero, mean, or median imputation — delivered as two independent analytical reports for up to 200 rows.

Runs two analytical reports on time series completeness data (observed vs expected points): a correlation analysis between the two measures and a keyed dataset comparison, for up to 200 rows.

Retrieves structured academic citation records about time series forecasting from Crossref publisher metadata, returning DOI references and source attribution.

Validates that all category/project values in a timesheet CSV fall within a declared allowlist of permitted categories.

Validates CSV timesheet data by auditing cell lengths against a configurable maximum, checking for oversized cells across up to 1000 rows.

Validates and audits a CSV timesheet import file against a declared contract, checking up to 1000 rows for errors, format issues, and constraint violations.

Validates a CSV timesheet file against a declared column schema contract, checking up to 1000 rows for structural and data integrity.

Validates a timesheet CSV file and returns a located error report identifying which cells contain invalid or out-of-range data.

Validates numeric fields in a timesheet CSV against configurable minimum and maximum bounds, returning a range check report for up to 1000 rows.

Validates that all record keys in a timesheet CSV are unique, returning a uniqueness report for up to 1000 rows.

Validates that all required cells are present in a timesheet CSV, checking up to 1000 rows against a declared contract.

Validates a CSV timesheet file for spreadsheet formula injection risks, flagging any cells that contain formula-like patterns.

Validates and normalizes a timesheet CSV file by trimming whitespace, inferring types, and checking values against declared constraints for up to 1000 rows.

Validates a CSV file of timesheet records (up to 1000 rows) and returns a structured summary report covering format errors, blank rows, numeric range issues, and category violations.

Validates a timesheet CSV against a declared contract (up to 1000 rows) and returns a report of all valid rows.

Returns the current local time, IANA timezone name, UTC offset, DST status, and next DST transition for Amsterdam.

Returns the current local time, IANA timezone name, UTC offset, DST status, and next DST transition for Bangkok