Time Series Analysis
Chapter notes, video classes, MCQ practice tests and quick-revision one-liners for Advanced Bank Management — CAIIB.
One-liners from this chapter
Free sample — 8 of 67 rapid-fire Q&A cards.
Define time series analysis in banking context.
Statistical technique applied to data recorded against a time base to forecast deposits, credit, NPAs, and other metrics.
What are the two variables in every time-series problem?
Dependent variable (value measured: deposits, NPA %, exchange rate) and independent variable (time).
Name four purposes of time series analysis in banking.
Review historical baseline, study past behaviour, compare across periods, predict future, forecast trade cycles.
Define secular trend and distinguish linear from curvilinear.
Long-period smooth direction. Linear: constant rate of change. Curvilinear: rate of change itself shifts over time.
What is cyclical variation? State typical duration and average business cycle length in India.
Wave-like movements with no fixed periodicity, unequal amplitude, span >1 year. India's cycle averages 5–9 years.
Name the four trade-cycle phases.
Prosperity → Recession → Depression → Recovery.
Define seasonal variation and give three banking examples.
Patterns repeating within a year with regular, predictable timing. ATM spikes on 1st/last workday; jewellery loans Aug–Oct; advance-tax outflows March.
What is deseasonalisation and why does RBI apply it?
Removing established seasonal pattern from series to expose underlying trend. RBI publishes seasonally adjusted IIP and money supply for month-on-month apples-to-apples comparison.
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