How Time‑Series Analysis Powers AIOps: Overcoming Real‑World Challenges

Time‑series data analysis is a core component of big‑data analytics and a key driver of digital transformation. At the 16th GOPS Global Operations Conference in Shenzhen, Shen Hui, Technical Product Director of DingMao Technology, used time‑series analysis as a lens to discuss the difficulties and challenges enterprises face in intelligent operations, and shared real‑world cases demonstrating the value of DingMao’s intelligent‑operation technology.

Time‑series data consists of measurement values paired with timestamps, enabling the extraction of patterns that evolve over time. By analyzing these patterns, organizations can perform trend forecasting and anomaly detection to deliver valuable services.

Time‑series analysis follows four steps: data collection → data governance → data analysis → data visualization. Data analysis is the core of AIOps, employing statistical learning, machine learning, and deep learning for regression, classification, feature extraction, and pattern matching. The analysis faces four major challenges: integrity checks during streaming collection, deduplication, denoising, missing‑point interpolation, and time‑series aggregation alignment.

To address these challenges, DingMao proposes a three‑pipeline architecture: a data pipeline, an ML pipeline, and an Application pipeline. The edge‑collection layer implements the full data pipeline, the ML pipeline leverages the data‑analysis layer, and the Application pipeline consists of scenario‑driven intelligent products. Expert feedback is incorporated to validate ML results and continuously optimize the models, forming a closed‑loop AIOps solution.

In practice, customer requirements lead to two deeper research directions. One builds a fault matrix from the relationships among multiple time‑series metrics to enable early warning of potential failures. The other focuses on trend forecasting: after establishing inter‑metric relationship patterns, the model extends these patterns forward in time to predict short‑term trends, assess capacity needs, and plan IT resource allocation.

These approaches underscore that robust time‑series analysis requires a powerful intelligent‑analysis platform.

Successful digital transformation cases share common traits: emphasis on data quality, contextual data, and an effective data‑operation platform. DingMao’s Arcana platform integrates strong AI algorithms to support the entire AI‑driven lifecycle—from data ingestion and processing, through model building, evaluation, and management, to scenario design and intelligent application—creating a complete, automated operational loop that continuously refines models.

Founder Li Yao emphasized that every industry’s digital‑transformation journey needs a professional, scenario‑aware partner; DingMao’s deep industry experience and extensive scenario library enable rapid, efficient implementation of intelligent solutions, helping clients break through the “deep water” of digital transformation and generate greater commercial value.