Classic Time Series Analysis Methods with Implementation Examples

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Resource Overview

This article introduces fundamental time series methodologies including Autoregressive (AR), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) models for stationary forecasting problems, accompanied by practical code implementations for immediate application.

Detailed Documentation

This article presents classic time series analysis methods, covering Autoregressive (AR) models, Autoregressive Moving Average (ARMA) models, and Autoregressive Integrated Moving Average (ARIMA) models. We provide detailed explanations of how these models address various stationary forecasting challenges, along with practical code implementations featuring key functions like parameter estimation and model diagnostics. The implementation examples include Python code utilizing libraries such as statsmodels for ARIMA parameter optimization and sklearn for evaluation metrics. Additionally, we analyze the advantages and limitations of each model's algorithmic approach, such as ARMA's combination of autoregressive and moving average components for handling serial correlation. The article concludes with future research directions in time series forecasting, offering valuable insights for researchers in related fields.

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