Time Series

🌐 Master Time Series with Dr. Mohd Saifi Anwar's "Valuation of Time Series: Advanced" 📊

Course Introduction 🚀

Time marches on, and with it come changes that shape our world in countless ways. From politics to economics, and from social dynamics to business trends, time series analysis is a crucial tool for understanding and forecasting the rhythm of these shifts. In Dr. Mohd Saifi Anwar's course, you'll dive deep into the realm of time series to unlock the secrets behind historical data patterns and their predictive power.

What is Time Series? 📈

Definition: Time series refers to the statistical analysis of data points collected over intervals of time. These data can be daily, weekly, monthly, quarterly, or yearly, providing a chronological insight into various phenomena.

Two Types of Variables: In time series, we focus on two types of variables - Discrete Time Series and Continuous Time Series.

The Importance of Time-Series Analysis 📊

• Understanding Past Transactions: Time series data allows us to visualize past trends, helping us make informed decisions.
• Forecasting and Planning: By analyzing patterns over time, we can predict future events with greater accuracy.
• Evaluation of Current Achievement: Time series provides a benchmark for assessing current performance relative to historical data.
• Making Comparative Studies: It enables us to compare different scenarios or entities across various points in time.

Causes of Variations in Time-Series Data 🌀

Variations Arise Due To:

1. Period: Long-term changes due to time.
2. Nature: The inherent characteristics of the variables.
3. Causes of Creation: External factors influencing the data, such as economic policies or technological advancements.
4. Regularities: Predictable patterns within the data, including seasonal and cyclical effects.

Components of Time Series 🔍

• Long Term or Secular Trend: Long-lasting shifts in the overall direction of the time series.
• Short-time Oscillation:
  • Seasonal Variations: Predictable patterns occurring over a short period, like quarterly or annually.
  • Cyclical Fluctuations: Patterns that repeat over longer periods but are shorter than the secular trend.
• Irregular or Random Fluctuations: Unpredictable changes in data, which can be episodic or accidental.

Analysis of Time-series 🧐

• Mathematical Models: Understanding the two main models - Multiplicative Model and Additive Model.
  • O = T x S x C x I for Multiplicative (where O is the original data, T is the trend, S is seasonality, C is cyclical variations, and I is irregular movements).
  • O = T + S + C + I for Additive.

Measurement of Secular Trend 📊

Several methods to measure the secular trend include:

1. Free hand curve method
2. Semi-Average method
3. Moving Average method
4. Method of Least Squares

Some Important Practical for Revision 📚

To wrap up, this course will provide you with the essential tools and knowledge to master time series analysis. With a blend of theoretical understanding and practical applications, you'll be well-equipped to interpret complex datasets and make data-driven decisions.

Enroll in "Valuation of Time Series: Advanced" today and unlock the potential of your data! 🎓✨