Time Series Analysis
Time series analysis (TSA) is discipline that is utilized across various industries, government bodies, and academic institutions. TSA applications include areas such as inventory and demand planning, marketing strategy formulation, capital allocation, pricing strategies, predictive maintenance of machinery, and macroeconomic predictions, among others. Forecasting relies heavily on time series data, which is now prevalent in numerous domains, from weekly unemployment claims and minute-by-minute stock prices to daily sales figures, wearable device activity logs, sensor-recorded machine performance, and key business performance metrics.
This course offers a comprehensive introduction to time series analysis and forecasting. It delves into the unique characteristics of time series data compared to cross-sectional data, methods for manipulating time series data, exploratory analysis techniques using statistical measures and their graphical representations, and the fundamental statistical models for time series analysis. You'll start by exploring the basics of time series, including data structure, preprocessing steps, and feature engineering through data wrangling. The course will then cover traditional time series methods such as AR, MA, ARMA, and ARIMA models, equipping you with the knowledge to handle real-world time series data and perform accurate forecasting.
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Course Instructor
Dr. Rahul Rai
CEO AIBrilliance
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