CORE

Time Series Analysis and Forecasting

24CSE881 · S8 · 4 credits

Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.

§
Syllabus
Loading syllabus…
Manage →
⚡ DAY-BEFORE EXAM

Have an exam tomorrow?

Get a high-yield attack plan focused on maximum marks per minute. Zero fluff, strict PYQ priority, active recall, and trap avoidance.

Strategy: High-yield concepts + PYQs + weak areas

Tap a module to mark as weak
PRIORITY A
Module 1

Why:Foundation module

+ Mark Weak
PRIORITY A
Module 2

Why:High exam relevance

+ Mark Weak
PRIORITY B
Module 3

Why:Core module

+ Mark Weak
PRIORITY B
Module 4

Why:Applied concepts

+ Mark Weak
PRIORITY C
Module 5

Why:Standard exam coverage

+ Mark Weak
Modules
Module 01Introduction to Time Seriesexpand

Introduction to time series, Stationary models and autocorrelation function, Estimation and elimination of trend and seasonal components Stationary Process and ARMA Models, Basic properties and linear processes, Introduction to ARMA models, properties of sample mean and autocorrelation function, Forecasting stationary time series, ARMA(p, q) processes, ACF and PACF, Forecasting of ARMA processes

Module 02Spectral Analysisexpand

Spectral Analysis, Spectral densities, Time-invariant linear filters, The spectral density of an ARMA process Modeling and Forecasting with ARMA Processes, Preliminary estimation, Maximum likelihood estimation, Diagnostics, Forecasting, Order selection

Module 03Nonstationary Time Series Modelsexpand

Nonstationary and Seasonal Time Series Models, ARIMA models, Identification techniques, Unit roots in time series, Forecasting ARIMA models, Seasonal ARIMA models, Regression with ARMA errors

Module 04Multivariate Time Series Analysisexpand

Multivariate Time Series, Second-order properties of multivariate time series, Estimation of the mean and covariance, Multivariate ARMA processes, Best linear predictors of second-order random vectors, Modeling and forecasting

Module 05State-Space Models and Forecasting Techniquesexpand

State-Space Models, State-space representations, The basic structure model, State- space representation of ARIMA models, The Kalman Recursions, Estimation for state- space models Forecasting Techniques, The ARAR algorithm, The Holt-Winter algorithm, The Holt- Winter seasonal algorithm

Notes / Resources

Exam-focused notes are optional context — Continue Learning above works even without them.

Notes for this subject aren't written yet

Continue Learning above adapts to this subject anyway — it teaches from your active syllabus.