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Lecture By
DAVINDER KAUR
Associate Professor in Economics

Time series analysis is a statistical technique that involves studying and analyzing data points collected over time to identify patterns, trends, and make predictions. Various methods are used to measure and analyze time series data:

1. Freehand Curve Method:
This method involves drawing a smooth curve through the data points by hand to identify trends and patterns.
*Application:* Useful for obtaining a visual understanding of the overall direction of the time series.

2. Semi-Average Method:
In this method, consecutive data points are grouped together, and the average of each pair is calculated. The resulting values form a new time series, helping to smooth out fluctuations.
*Application:* Used to reduce noise in the data and highlight underlying trends.

3. Moving Average Method:
This method calculates the average of a specified number of consecutive data points (the "window" or "lag"). As new data points become available, the average is recalculated by shifting the window forward.
*Application:*
*Simple Moving Average (SMA):* Provides a smoothed representation of the time series.
*Weighted Moving Average (WMA):* Assigns different weights to different data points, emphasizing the importance of certain observations.
*Exponential Moving Average (EMA):* Places more weight on recent data, making it responsive to changes.

These methods are particularly useful for identifying trends and patterns in time series data by reducing noise and highlighting underlying structures. The choice between methods depends on the characteristics of the data and the specific objectives of the analysis.