What will you learn in this Practical Time Series Analysis Course
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Understand the fundamentals of time series analysis, including concepts like stationarity, autocorrelation, and seasonality.
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Apply statistical models such as Moving Average (MA), Autoregressive (AR), ARMA, ARIMA, and SARIMA to real-world data.
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Utilize R programming for data visualization, model building, and forecasting.
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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
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Equips learners for roles such as Data Analyst, Forecasting Analyst, and Quantitative Researcher.
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Applicable in industries like finance, economics, environmental science, and supply chain management.
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Enhances employability by providing practical skills in time series modeling and forecasting.
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Supports career advancement in fields requiring expertise in temporal data analysis.
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Time Series Analysis with Python Course – Apply Python to analyze and forecast time-dependent data, exploring techniques like ARIMA, Prophet, and LSTM models.
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