Uses & Limitations of Time Series ⚖️

While Time Series analysis is a potent tool for forecasting, it is not without its flaws.

Utility / Uses 🌟

1. Analysis of Past Behavior 📜

It enables us to understand the past behavior of a variable. By observing past trends, we can determine the causes of variations.

2. Forecasting Future 🔮

This is the most important use. It helps in predicting future values (e.g., sales, demand, population) which is essential for:

• Budgeting
• Production Planning
• Inventory Control

3. Evaluation of Performance 📊

We can compare the Actual Performance with the Expected (Trend) Performance.

• Example: If actual sales are widely below the trend line, it indicates poor performance or an external shock.

4. Comparative Study 🆚

It allows for comparison between different time series.

• Example: Comparing the growth rate of India's GDP vs China's GDP over the last 10 years.

Limitations 🚧

1. Based on Past 🔙

It assumes that "History repeats itself". It assumes that the factors affecting the past will continue to affect the future in the same way. This is not always true (e.g., COVID-19 disrupted all past trends).

2. Ignores External Factors 🌍

Simple trend analysis often ignores qualitative factors like changes in technology, government policy, or consumer tastes unless complex models are used.

3. Not Precise 🎯

Forecasts are never 100% accurate. They are merely estimates based on probability.

4. Computationally Complex 🧮

Methods like Least Squares can be tedious to calculate manually for large datasets.

Summary

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