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所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-analysis-l/
课程评论：没有评论
课程名称：时间序列分析 课程概述：时间序列分析（TSA）是一个重要的研究领域，在多个行业、政府及教育领域有广泛应用。它帮助进行多种任务，如库存与需求规划、营销策略制定、资本分配决策、定价设置、设备维护以及经济预测。随着时间戳数据的增加，例如每周失业申请、逐分钟股票价格、每日销售、传感器数据以及可穿戴设备日志，预测越发依赖于数据。 本课程提供了时间序列分析与预测的清晰介绍。学习者将了解时间序列数据与其他类型数据的特殊特征，以及如何处理和分析时间序列数据集。课程内容涵盖基本统计量、可视化技术以及时间序列分析中使用的关键统计模型。 课程从基础入手，包括时间序列数据的结构、数据清理与准备，及如何从数据中创建有用特征。随着课程的深入，学习者将获得处理常见时间序列模型的实际经验，如自回归（AR）、移动平均（MA）、自回归移动平均（ARMA）以及自回归积分移动平均（ARIMA）模型。课程结束时，学习者将掌握分析实时序列数据和创建准确预测模型的技能。
Time Series Analysis (TSA) is an important area of study used in many fields, including industries, government, and education. It helps with various tasks such as planning inventory and demand, creating marketing strategies, deciding how to allocate capital, setting prices, maintaining machinery, and forecasting the economy. With the rise of time-stamped data, like weekly unemployment claims, minute-by-minute stock prices, daily sales, sensor data, and wearable device logs, forecasting relies more on data than ever.This course offers a clear introduction to time series analysis and forecasting. You will learn about the special features of time series data compared to other types of data and how to handle and analyze time series datasets. The course includes basic statistical measures, visualisations, and key statistical models used in time series analysis.You will start with the basics, including how time series data is structured, how to clean and prepare data, and how to create useful features from it. As the course continues, you will gain practical experience with common time series models, such as Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) models. By the end of the course, you will know how to analyse real-time series data and create accurate forecasting models.
