Automated Financial Time-Series Data Extraction from Semi-Structured Tables
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The diversity and variability of tabular data structures in enterprise settings make it costly and inefficient to standardize and unify financial time-series data, as custom pre-processing pipelines are rarely reusable across different datasets and formats.
Innovation Solution
A method and system for automated extraction and conversion of financial time-series data from semi-structured tabular inputs into a unified standard format, involving cell segmentation, header and content cell determination, normalization of time periods and metrics, and generation of output tables in formats like JSON or tab-delimited files, using a processor and dateparser library for date identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If custom pre-processing pipelines are built for each individual dataset, then data standardization can be achieved for that specific dataset, but the cost and time consumption increase significantly
Solution Approach 1:
The patent creates a universal pre-processing pipeline that can handle multiple different tabular data formats and structures through a single system. The methodology identifies and extracts headers and content cells from various tabular inputs (Excel, CSV, TSV) and transforms them into a unified format, making the pipeline reusable across different datasets, business units, and functions without requiring custom development for each case.
Solution Approach 2:
The patent segments tabular data into distinct header cells and content cells through automated identification. By dividing the data structure into these fundamental components and processing them separately through normalization and transformation steps, the system can handle diverse tabular formats while maintaining consistent output standards, thereby improving both precision and reusability.
2Reliability
If custom pre-processing pipelines are built for each business unit and function, then specific data requirements are met, but the overall system complexity and development cost increase
Solution Approach 1:
The patent develops a single versatile pre-processing pipeline that serves multiple business units and functions. The system automatically adapts to different data formats and structures through its header and content cell identification methodology, eliminating the need for separate custom pipelines for each business unit while maintaining reliable data transformation quality.
Solution Approach 2:
The patent implements a self-serve automated tool that enables business users to independently process and standardize their own tabular data without requiring custom pipeline development. The system automatically identifies data structures, normalizes headers and content, and transforms outputs into unified formats, allowing users to serve their own data standardization needs without external technical intervention.
3Manufacturing precision
If manual data standardization processes are used, then data accuracy can be maintained, but the time and resource consumption increase
Solution Approach 1:
The patent creates an automated self-serve system that performs data standardization without manual intervention. The methodology automatically identifies header and content cells, normalizes data formats, and transforms outputs into unified structures, maintaining data accuracy through systematic processing rules while eliminating the time and resources required for manual standardization efforts.
Solution Approach 2:
The patent replaces manual mechanical data processing with an automated computational system. The methodology uses algorithmic approaches to identify data structures, normalize values, and transform formats, substituting human manual work with automated processing that maintains precision while dramatically reducing processing time and resource consumption.
Data Source
AI summary
A method and a system for extracting and formatting financial time-series data is provided. The method includes: receiving a first set of tabular data; segmenting the first set of tabular data into a plurality of cells; determining whether each respective cell is a header cell or a content cell; for each header cell, normalizing each of a time period and a metric associated therewith; for each content cell, determining one or more column header cells and one or more row header cells associated. therewith; and generating an output table based on a result thereof.