What will you learn in this Practical Time Series Analysis Course
Understand the fundamentals of time series analysis, including concepts like stationarity, autocorrelation, and seasonality.
Apply statistical models such as Moving Average (MA), Autoregressive (AR), ARMA, ARIMA, and SARIMA to real-world data.
Utilize R programming for data visualization, model building, and forecasting.
Implement techniques for model selection, parameter estimation, and diagnostic checking.
Program Overview
1. Basic Statistics
⏳ 3 hours
Review essential statistical concepts and get started with R programming.
2. Visualizing Time Series and Beginning to Model Time Series
⏳ 3 hours
Learn to visualize time series data and introduce basic modeling techniques.
3. Stationarity, MA(q), and AR(p) Processes
⏳ 5 hours
Delve into stationarity concepts and explore Moving Average and Autoregressive processes.
4. AR(p) Processes, Yule-Walker Equations, PACF
⏳ 5 hours
Understand the Yule-Walker equations and Partial Autocorrelation Function for AR models.
5. Akaike Information Criterion (AIC), Mixed Models, Integrated Models
⏳ 5 hours
Learn about model selection using AIC and explore ARMA and ARIMA models.
6. Seasonality, SARIMA, Forecasting
⏳ 4 hours
Address seasonal components in time series and implement SARIMA models for forecasting.
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Job Outlook
Equips learners for roles such as Data Analyst, Forecasting Analyst, and Quantitative Researcher.
Applicable in industries like finance, economics, environmental science, and supply chain management.
Enhances employability by providing practical skills in time series modeling and forecasting.
Supports career advancement in fields requiring expertise in temporal data analysis.
Specification: Practical Time Series Analysis
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