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3 Year Moving Average Calculator

3 Year Moving Average Formula:

\[ 3Y\ MA = \frac{\sum Last\ 36\ Months}{36} \]

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1. What is 3 Year Moving Average?

The 3 Year Moving Average (3Y MA) is a statistical calculation that smooths out fluctuations in data by averaging values over a 36-month period. It's commonly used in finance, economics, and business to identify long-term trends by reducing the impact of short-term volatility.

2. How Does the Calculator Work?

The calculator uses the simple moving average formula:

\[ 3Y\ MA = \frac{\sum Last\ 36\ Months}{36} \]

Where:

Explanation: The calculation adds up all values from the most recent 36 months and divides by 36 to find the average.

3. Importance of 3Y MA Calculation

Details: The 3-year moving average helps identify underlying trends by smoothing out seasonal variations and short-term fluctuations. It's particularly useful for financial analysis, sales forecasting, and economic indicators.

4. Using the Calculator

Tips: Enter at least 36 monthly values (separated by commas or new lines). The calculator will automatically use the most recent 36 values if more are provided. Select your currency for proper labeling of results.

5. Frequently Asked Questions (FAQ)

Q1: Why use 3-year instead of shorter moving averages?
A: A 3-year average provides a longer-term perspective that better filters out short-term volatility while remaining responsive to genuine trends.

Q2: What's the difference between simple and weighted moving averages?
A: This calculator uses simple MA where all months are weighted equally. Weighted MAs give more importance to recent data.

Q3: Can I use this for weekly or daily data?
A: While possible, you'd need to adjust the period (e.g., 156 weeks for 3 years). This calculator is designed for monthly data.

Q4: How does 3Y MA compare to other smoothing techniques?
A: 3Y MA is simpler than exponential smoothing but may lag more behind turning points in trends.

Q5: What are common applications of 3Y MA?
A: Common uses include analyzing company revenues, economic indicators, stock prices, and any time series data where long-term trends matter.

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