B.TechSemester 72023-24Time Series Analysis And ForecastingKAI076
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Time Series Analysis And Forecasting (KAI076) - AKTU Question Paper 2023-24

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Time Series Analysis And Forecasting Previous Year Question Paper for B.Tech Semester 7, academic session 2023-24. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 7
Session:2023-24
University:AKTU / UPTU

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Questions Asked in 2023-24

Time Series Analysis And Forecasting (KAI076) — complete question paper · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20
  • a
    What are the different types of data used in time series analysis? 2
  • b
    Define autocorrelation in the c ontext of time series data. 2
  • c
    What metrics would you use to evaluate the performance of a fo recasting model?
  • d
    What is the purpose of plotti ng smoothed data in time series analysis? 2
  • e
    What is meant by least squares es timation in linear regression models? 2
  • f
    What is variable selection met hods used in regression analysis? 2
  • g
    How to identify order o f an ARIMA model? 2
  • h
    Define stationarity in context of ARMA models. 2 i. What is a seasonal ARIMA model? 2 j. What are vector AR models? 2
Section BAttempt any three o f t h e f o l l o w i n g : 10 x 3 = 30
  • a
    Define a time series and discuss its structure due to trend, seasonality, cyclic changes, and irregular components. Explain how these elements i nfluence analysis and prediction
  • b
    Outline the standard approach for creating a time series for ecast model, including steps from graphical analysis and pre-processing to m odel identification, parameter estima tion, diagnostic checking, and finally, forecasting
  • c
    Explain the process of model ad equacy checking in linear regression models. 10
  • d
    Outline strengths and limitations of ARIMA forecasts for sho rt and long term horizons
  • e
    Describe the process of predicting with a seasonal ARIMA mod el, including the use of past seasonal factors. Give examples of real-world s easonal time series data
Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10 x 1 = 10
  • a
    Discuss the nature and common uses of forecasting. What are some examples where forecasting techniques are applied in real world scenarios?
  • b
    Explain the forecasting process in detail, highlighting the key resources required and steps involved from data collection to model devel opment to forecast output
  • a
    What is the general framework applied for developing a time series forecasting model? Discuss the key steps involved starting from graphical analysis and pre-processing to m odel identification, parameter estimation and diagnostic checking before generating forecasts
  • b
    Explain why data transformations like differencing and adjus tments for inflation, holidays etc. may need to be applied as part of deve loping a time series forecasting model. Provi de suitable examples to support your explanation
  • a
    Discuss first and second-orde r exponential smoothing models. 10
  • b
    Discuss generalized and weighted least squares regression fo cusing on situations like heteroscedastici ty and autocorrelation where th ey become relevant along with differences in formulation/calculations com pared to ordinary least squares method
  • a
    Discuss the core concepts behind Autoregressive (AR) and Mov ing Average (MA) models. Explain how they deal with serial correlation patterns and how they are combined in an ARMA model by using lagged values and errors
  • b
    Explain how autocorrelation an d partial autocorrelation plot s can help determine important lags to include as AR or MA terms. Also dis cuss model fitting, estimation and adequacy checking
  • a
    Elaborate on key properties of multivariate time series mode ls highlighting necessity when multiple interdependent series influence each ot her. Also contrast univariate vs multivariate forecasting
  • b
    Discuss the process of model selection in time series analys is outlining criteria like AIC, BIC, MAPE etc. used to compare model perform ance on calibration data

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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