How to Smoothly Distribute Monthly Data Values Across Daily Intervals?

  • Thread starter josse34
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In summary, the individual has a problem to calculate the daily value of a given year based on monthly values that must match the previously described values. They also want the graphic to have a smoothing line, such as spline or Simpson's rule. They are open to any method, with simplicity being preferred for integration into PHP code. They are seeking ideas and thank the reader for their time.
  • #1
josse34
2
0
Hi!
I have a problem to resolve this question :

I have a total value each month :
January = 60
Fébruary = 80
March = 130
April = 150
May = 180
June = 200
July = 210
August = 170
September = 140
October = 90
November = 60
December = 50

I want to calculate the value each day of year, but the total of this values each month must be exactly like value previously describing.
Also, the graphic render must be with smoothing line, like spline or simpson rule.

See attached a excel base http://cjoint.com/?CKilaalz7z2

How to make this ?!

Thank you so much and have a nice day!

Bye
 
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  • #2
Simpson's rule is for numerical integration, so you don't want that.
 
  • #3
Any method is ok for me, the simply is the best because i must integrate this into PHP code after!
Do you any idea?
Thanks
 

FAQ: How to Smoothly Distribute Monthly Data Values Across Daily Intervals?

1)

What is data smoothing and why is it important for analyzing yearly trends?

Data smoothing is a statistical technique used to remove noise and fluctuations in data, resulting in a clearer pattern or trend. It is important for analyzing yearly trends because it helps to identify the underlying patterns and relationships in the data without being influenced by random variations.

2)

How does data smoothing work and what methods are commonly used for smoothing yearly data?

Data smoothing typically involves applying mathematical algorithms or filters to a dataset. Common methods for smoothing yearly data include moving averages, exponential smoothing, and polynomial smoothing. These methods use different approaches to remove noise and reveal underlying trends in the data.

3)

What are the potential drawbacks of data smoothing for yearly data?

While data smoothing can help to identify underlying trends, it can also potentially distort or hide important information in the data. Additionally, different smoothing methods may produce different results, making it important to carefully consider which method is most appropriate for the specific dataset.

4)

Can data smoothing be used to predict future trends based on yearly data?

Yes, data smoothing can be used for forecasting future trends based on historical data. However, it is important to note that predictions are only as reliable as the data used for smoothing and may be impacted by unforeseen events or changes in the data.

5)

How can data smoothing be applied in real-life situations for analyzing yearly data?

Data smoothing can be applied in various fields such as finance, economics, and weather forecasting to analyze and predict yearly trends. For example, it can be used to identify patterns in stock market trends or predict seasonal variations in consumer spending.

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