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
UNIT II: Trend Analysis and Time Series Decomposition
Trend Analysis: Meaning and importance of Trend
Types of Trend (Upward, Downward, Horizontal).
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