The article introduces time‑series data, its ubiquitous examples, the challenges of traditional small models, and proposes a universal time‑series large model that simplifies data preparation and model building, ultimately enabling more efficient and stable industrial AI solutions, now offered as a cloud service.
This article systematically explores the principles and mathematics behind ARIMA, XGBoost, LSTM, and Transformer models, compares their strengths and weaknesses, and demonstrates a complete end‑to‑end workflow for detecting CPU resource anomalies in a cloud service environment.
This article explains the concept of differencing for extracting deterministic information, outlines when to use first, second, or seasonal differencing, introduces the ARIMA(p,d,q) model and its special cases, and provides a complete Python example with code and visualizations.
This tutorial walks through the full Python-based ARIMA modeling process for time‑series analysis, covering data loading, stationarity and white‑noise tests, model order selection, parameter estimation, diagnostic checks, and future forecasting with detailed code examples.
This article outlines Ctrip's approach to time series forecasting, covering background, common tools such as factor‑based models, traditional statistical methods like ARIMA, and machine‑learning techniques including tree and neural networks, and shares practical experiences on data splitting, feature engineering, model stability, and evaluation.
This article introduces time series fundamentals, classification, and challenges for internet businesses, then provides a step‑by‑step Python tutorial on ARIMA modeling—including data loading, stationarity testing, differencing, ACF/PACF analysis, AIC‑based order selection, model training, prediction, error evaluation, exogenous variable integration, and diagnostic checks.
This article walks through a comprehensive analysis of a 3.43 GB sales dataset from a Brazilian automotive service chain, covering data loading, cleaning, exploratory visualizations, time‑series forecasting with ARIMA, RFM customer segmentation, product clustering, and key business insights using Python and Tableau.
The article presents a complete workflow for predicting next‑day API request volumes by exploring per‑minute traffic data, handling missing values, applying seasonal decomposition, training an ARIMA model on the trend component, and generating confidence intervals to flag anomalous spikes.
iQIYI’s brand‑advertising system combines statistical and ARIMA‑based forecasting, adaptive and deep‑learning models, factorization‑machine regression, large‑scale bipartite‑graph allocation, hierarchical handling of long‑tail dimensions, frequency‑capping constraints, and an AI‑driven video‑tagging pipeline to accurately estimate inventory and dynamically place ads.
The article reviews traditional, neural network, and open‑source time‑series forecasting methods, explains their strengths and limitations, and demonstrates how NetEase applies short‑term and long‑term prediction models such as Holt‑Winters, ARIMA, STL, Prophet, and LSTM to improve game monitoring and proactive alerting.
This article explains how to process a periodic time‑series, split it into training and test sets, smooth the data, decompose it with statsmodels' seasonal_decompose, forecast the trend using an ARIMA model, and evaluate the results with RMSE, providing a practical workflow for accurate forecasting.
This article explains how 360’s DoctorStarange system combines time‑series forecasting, neural‑network predictions, alarm correlation, and a machine‑health scoring model to reduce false alerts, automate remediation, and maximize resource utilization across thousands of production servers.
