Time Series Analysis
This page provides a detailed explanation of the Time Series Analysis (TSA) syllabus with simple notes, easy-to-understand concepts, illustrative examples, and practical applications for BCA III Semester (NEP 2.0) students.
UNIT I: Introduction to Time Series
Concept of Time Series: Definition and meaning of Time Series, Characteristics of Time Series Data.
Types of Time Series: Annual Time Series, Quarterly Time Series, Monthly Time Series, Daily Time Series.
Components of Time Series: Trend (T), Seasonal Variation (S), Cyclical Variation (C), Irregular Variation (I).
Applications of Time Series Analysis: Educational Data Analysis, Business and Economic Forecasting, Student Performance and Enrollment Analysis.
UNIT II: Trend Analysis and Time Series Decomposition
Trend Analysis: Meaning and importance of Trend, Types of Trend (Upward, Downward, Horizontal). Methods of Measuring Trend: Graphical Method, Semi-Average Method, Moving Average Method, Least Square Method. Time Series Decomposition Models: Additive Model, Multiplicative Model. Practical Applications of Trend Analysis: Enrollment Trend Analysis, Result Performance Analysis.
UNIT III: Seasonal, Cyclical, and Irregular Variations
Seasonal Variations: Concept of Seasonal Index, Methods of Measuring Seasonal Variations: Simple Average Method, Ratio to Moving Average Method, Ratio to Trend Method. Cyclical Variations: Meaning and characteristics, Business cycles and their impact on Time Series, Irregular Variations: Nature and causes of irregular changes, Impact of unexpected events on data. Interpretation of Time Series Data: Combined analysis of all components
UNIT IV: Forecasting and Modern Time Series Techniques
Concept of Forecasting: Short-term and Long-term Forecasting, Importance of Forecasting. Forecasting Techniques: Naïve Forecasting, Simple Moving Average, Simple Exponential Smoothing. Introduction to Statistical Time Series Models: Auto Regression (AR) – basic concept: Moving Average (MA) – basic concept, ARIMA Model – introduction, Time Series in Data Analytics: Role of Time Series in Data Science, Applications in Educational and Business Data, Future Scope in Machine Learning and AI