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time series

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Python Programming Learning Circle
Python Programming Learning Circle
May 23, 2025 · Artificial Intelligence

Useful Python Libraries for Data Science (Beyond pandas and NumPy)

This article introduces a curated list of lesser‑known Python packages for data‑science tasks—including Wget, Pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, Ipyvolume, Dash, and Gym—providing installation commands, brief usage examples, and explanations of when each library is useful.

LibrariesPythondata science
0 likes · 10 min read
Useful Python Libraries for Data Science (Beyond pandas and NumPy)
Architecture & Thinking
Architecture & Thinking
May 15, 2025 · Databases

Redis 8.0 Unveiled: New AGPLv3 License, Vector Search, JSON & More

Redis 8.0, released on May 1 2025, introduces a major license shift to AGPL‑v3, adds eight native data structures—including vector sets, JSON, and time‑series—enhances the query engine with up to 16× performance gains, improves scalability, security, and cloud‑native support, and provides extensive code examples for AI and real‑time analytics.

JSONRedisSecurity
0 likes · 15 min read
Redis 8.0 Unveiled: New AGPLv3 License, Vector Search, JSON & More
JD Tech
JD Tech
Apr 1, 2025 · Artificial Intelligence

Self‑Isolation Mechanism for Time‑Series Anomaly Detection with Memory Space

This article presents a self‑isolation based streaming anomaly detection framework that combines memory‑space indexing to capture pattern anomalies, long‑term memory, and concept drift in time‑series data, and validates the approach with public benchmarks and real‑world risk‑control scenarios.

Anomaly Detectionconcept driftmemory space
0 likes · 24 min read
Self‑Isolation Mechanism for Time‑Series Anomaly Detection with Memory Space
Python Programming Learning Circle
Python Programming Learning Circle
Mar 24, 2025 · Artificial Intelligence

Comprehensive List of Aggregation Functions and Custom Feature Engineering Utilities for Python

This article presents a detailed collection of built‑in pandas aggregation methods and numerous custom Python functions for time‑series feature engineering, offering beginners practical tools to enhance data preprocessing and model performance in machine‑learning projects.

aggregation functionsdata sciencefeature engineering
0 likes · 10 min read
Comprehensive List of Aggregation Functions and Custom Feature Engineering Utilities for Python
Soul Technical Team
Soul Technical Team
Jan 24, 2025 · Operations

Migration from Thanos to VictoriaMetrics: Architecture, Plan, Issues, and Benefits

This article details the end‑to‑end migration from Thanos to VictoriaMetrics, covering background analysis, architectural comparison, a phased migration plan, encountered configuration and performance issues, resolution strategies, and the resulting performance, cost, and scalability improvements for the monitoring system.

MigrationThanosVictoriaMetrics
0 likes · 16 min read
Migration from Thanos to VictoriaMetrics: Architecture, Plan, Issues, and Benefits
Test Development Learning Exchange
Test Development Learning Exchange
Nov 29, 2024 · Artificial Intelligence

Using LSTM Networks for Stock Price Time Series Prediction with Keras

This tutorial demonstrates how to apply an LSTM deep‑learning model in Python to forecast stock closing prices, covering data acquisition, preprocessing, model construction, training, evaluation, and visualization of results for time‑series prediction.

AIKerasLSTM
0 likes · 8 min read
Using LSTM Networks for Stock Price Time Series Prediction with Keras
Test Development Learning Exchange
Test Development Learning Exchange
Nov 20, 2024 · Fundamentals

Processing Time Series Data with Pandas: Types, Slicing, Resampling, and Rolling Windows

This tutorial teaches how to handle time series data using pandas, covering datetime types, slicing by date ranges or months, various resampling frequencies, and rolling window calculations such as moving averages and standard deviations, followed by a practical stock price analysis example.

data analysispandasresampling
0 likes · 6 min read
Processing Time Series Data with Pandas: Types, Slicing, Resampling, and Rolling Windows
Python Programming Learning Circle
Python Programming Learning Circle
Sep 10, 2024 · Artificial Intelligence

Time Series Feature Engineering Techniques in Python

This article explains how to extract a variety of date‑time based features—including date, time, lag, rolling, expanding, and domain‑specific attributes—from a time‑series dataset using pandas, and discusses proper validation strategies for building reliable forecasting models.

Pythonfeature engineeringforecasting
0 likes · 14 min read
Time Series Feature Engineering Techniques in Python
DataFunTalk
DataFunTalk
Aug 1, 2024 · Artificial Intelligence

Ant Group's Time Series AI Practices: AntFlux Engine and Real‑World Applications

This article presents Ant Group's comprehensive time‑series AI solutions, detailing the AntFlux platform, the evolution from statistical to deep and large‑scale models—including Time‑LLM, iTransformer, and SLOTH—and illustrating how these technologies empower business insight, forecasting, decision‑making, and green computing across diverse scenarios.

AntFluxArtificial Intelligenceforecasting
0 likes · 17 min read
Ant Group's Time Series AI Practices: AntFlux Engine and Real‑World Applications
DataFunTalk
DataFunTalk
Jul 14, 2024 · Artificial Intelligence

Time Series and Machine Learning – An Overview and Book Introduction

The article introduces the rapid rise of large language models, the abundance of time‑series data in many sectors, and explains how combining machine‑learning and deep‑learning techniques with time‑series analysis has become a research hotspot, culminating in a new book that systematically covers theory, methods, and real‑world applications.

AIAnomaly Detectiondeep learning
0 likes · 10 min read
Time Series and Machine Learning – An Overview and Book Introduction
Model Perspective
Model Perspective
Jun 26, 2024 · Artificial Intelligence

Unlocking Fraud Detection: Build a Hidden Markov Model with Python

This article explains the fundamentals and mathematics of Hidden Markov Models, illustrates their core components and basic problems, and walks through a complete Python implementation for credit‑card fraud detection, including data preparation, model training, and evaluation.

Hidden Markov ModelPythonfraud detection
0 likes · 10 min read
Unlocking Fraud Detection: Build a Hidden Markov Model with Python
Python Programming Learning Circle
Python Programming Learning Circle
May 23, 2024 · Artificial Intelligence

Comprehensive Collection of Aggregation Functions for Feature Engineering in Python

This article presents a detailed compilation of pandas built‑in aggregation methods and a wide range of custom Python functions for time‑series feature engineering, providing ready‑to‑use code snippets that cover statistical descriptors, drawdown metrics, peak detection, and more for data science practitioners.

AggregationPythonfeature engineering
0 likes · 17 min read
Comprehensive Collection of Aggregation Functions for Feature Engineering in Python
DataFunTalk
DataFunTalk
Mar 19, 2024 · Big Data

High‑Performance Vehicle IoT Big Data Platform Solution Based on DolphinDB

This article presents a comprehensive vehicle‑IoT big‑data platform solution that outlines required capabilities, describes a DolphinDB‑based architecture, shares a real‑world case of 1.8 × 10⁸ writes per second, and provides step‑by‑step deployment and query scripts for rapid verification.

Data AnalyticsDolphinDBStreaming
0 likes · 18 min read
High‑Performance Vehicle IoT Big Data Platform Solution Based on DolphinDB
HelloTech
HelloTech
Mar 14, 2024 · Artificial Intelligence

Feature Engineering: Concepts, Methods, and Automation

Feature engineering transforms existing data into new predictive variables through manual analysis or automated pipelines, encompassing single‑variable encoding, pairwise arithmetic, group‑statistics, multi‑variable combinations, time‑series and text derivations, with tools like Deep Feature Synthesis and beam‑search to generate and select useful features.

automated featuresdata preprocessingfeature derivation
0 likes · 17 min read
Feature Engineering: Concepts, Methods, and Automation
DataFunTalk
DataFunTalk
Mar 11, 2024 · Artificial Intelligence

Anomaly Detection and Attribution Diagnosis Practices at Ant Financial

This article presents Ant Financial's practical approaches to anomaly detection and attribution diagnosis, detailing the underlying concepts, four methodological categories, specific algorithms such as VBEM, AnoSVGD and Autoformer, multi‑dimensional factor analysis, real‑world challenges, and operational benefits for KPI monitoring and incident response.

AIAnomaly DetectionAttribution Analysis
0 likes · 13 min read
Anomaly Detection and Attribution Diagnosis Practices at Ant Financial
Python Programming Learning Circle
Python Programming Learning Circle
Mar 7, 2024 · Fundamentals

Introduction to Time Series Analysis with a Python Example

This article explains the concept of time series, describes its main patterns such as long‑term trend, seasonal, cyclical and random variations, and demonstrates a practical Python analysis of a three‑year sales dataset from a Taobao shop using pandas and matplotlib.

Pythondata analysisseasonality
0 likes · 4 min read
Introduction to Time Series Analysis with a Python Example
DataFunSummit
DataFunSummit
Feb 12, 2024 · Artificial Intelligence

Ant Group's Time Series AI Practices and the AntFlux Intelligent Engine

This article presents Ant Group's comprehensive time‑series AI solutions, covering the business value of temporal data, the evolution of statistical and deep learning models, large‑scale time‑series platforms such as AntFlux, and real‑world applications ranging from financial forecasting to green computing.

AIAntFluxforecasting
0 likes · 17 min read
Ant Group's Time Series AI Practices and the AntFlux Intelligent Engine
Model Perspective
Model Perspective
Feb 1, 2024 · Artificial Intelligence

Discover Top Change & Prediction Model Articles for AI and Data Science

This article compiles a categorized list of recent model papers, covering change models and various prediction models—including time series, machine learning, gray prediction, and deep learning—providing direct references for students and researchers interested in AI and data‑driven modeling.

Artificial Intelligencemachine learningmodeling
0 likes · 6 min read
Discover Top Change & Prediction Model Articles for AI and Data Science
Baidu Geek Talk
Baidu Geek Talk
Jan 29, 2024 · Databases

BTS (Baidu Table Storage): Architecture and Core Technologies

BTS (Baidu Table Storage) is Baidu Intelligent Cloud’s high‑performance, low‑cost semi‑structured NoSQL service that evolved from single‑table to multi‑model (wide tables, time‑series, soon documents), featuring a three‑layer compute‑storage separation architecture, multi‑level caching, hot‑backup HA, and supporting massive IoT, AI, autonomous‑driving and monitoring workloads.

BTSBaidu Table StorageDatabase Architecture
0 likes · 21 min read
BTS (Baidu Table Storage): Architecture and Core Technologies
AntTech
AntTech
Dec 14, 2023 · Artificial Intelligence

Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023

The article summarizes Ant Group's twenty accepted NeurIPS 2023 papers, covering advances in generative AI, time‑series forecasting, 3D image synthesis, and other machine‑learning topics, and provides brief overviews of three highlighted works along with links to the remaining studies.

3D Image SynthesisAnt GroupNeurIPS
0 likes · 10 min read
Highlights of Ant Group’s 20 Accepted Papers at NeurIPS 2023