Devise sequence-rule detection strategy

Quick Overview

This question evaluates pattern-recognition, algorithm design, statistical reasoning, and model-selection skills required for sequence inference and candidate-term prediction in the Coding & Algorithms / Data Science domain.

Devise sequence-rule detection strategy

Company: Optiver

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Take-home Project

You are given an integer sequence with 2 ≤ n ≤ 50 terms, possibly following rules such as arithmetic/geometric progressions, alternating patterns, polynomial generation, digit-level transforms, interleaving of subsequences, or simple recurrences. Explain a systematic approach to infer the governing rule and predict the next k terms (1 ≤ k ≤ 5). Describe the heuristics you would apply in order (e.g., common-difference/ratio checks, finite differences, modulus patterns, alternating/parity splits, substring/digit operations), how you would detect insufficient evidence or multiple plausible rules, and how you would implement this as a program that outputs candidate rules scored by plausibility. Analyze time complexity and discuss how you would evaluate correctness under noisy or ambiguous sequences.

Quick Answer: This question evaluates pattern-recognition, algorithm design, statistical reasoning, and model-selection skills required for sequence inference and candidate-term prediction in the Coding & Algorithms / Data Science domain.

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Sep 6, 2025, 12:00 AM
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You are given an integer sequence with 2 ≤ n ≤ 50 terms, possibly following rules such as arithmetic/geometric progressions, alternating patterns, polynomial generation, digit-level transforms, interleaving of subsequences, or simple recurrences. Explain a systematic approach to infer the governing rule and predict the next k terms (1 ≤ k ≤ 5). Describe the heuristics you would apply in order (e.g., common-difference/ratio checks, finite differences, modulus patterns, alternating/parity splits, substring/digit operations), how you would detect insufficient evidence or multiple plausible rules, and how you would implement this as a program that outputs candidate rules scored by plausibility. Analyze time complexity and discuss how you would evaluate correctness under noisy or ambiguous sequences.

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