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Calcimator

Time Series Decomposition Calculator

Decompose monthly time series data into trend, seasonal, and residual components using additive or multiplicative decomposition methods.

Inputs

Results

Trend Slope

2.48

Seasonal Strength

82.41

MethodAdditive
Trend Intercept148.03
Average Trend Value164.17
Max Seasonal Index44.59
Min Seasonal Index-37.82
How to Use This Calculator
  1. Enter time series data with dates and values.
  2. Select the decomposition model: additive or multiplicative.
  3. Set the seasonal period (e.g., 12 for monthly data with annual seasonality).
  4. Review the trend, seasonal, and residual components.
  5. Use the trend component for forecasting and the residual to identify anomalies.

How the result changes with December

DecemberTrend SlopeSeasonal Strength
-800,000-30,772.13769,377.87
-300,000-11,541.36288,608.64
300,00011,535.56288,314.44
800,00030,766.33769,083.67

What each input means

January
Observed value for January.
February
Observed value for February.
March
Observed value for March.
April
Observed value for April.
May
Observed value for May.
June
Observed value for June.
July
Observed value for July.
August
Observed value for August.
September
Observed value for September.
October
Observed value for October.
November
Observed value for November.
December
Observed value for December.
Decomposition Type
Additive: Y = T + S + R. Multiplicative: Y = T × S × R. Use multiplicative when seasonal variation scales with the trend.

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    January = 120, February = 130, March = 145, April = 160 = 13 input(s) provided
  2. Calculate Trend Slope
    2.4825 = 2.4825
  3. Calculate Seasonal Strength
    Seasonal Strength
    82.4126 = 82.4126
  4. Calculate Method
    Additive = Additive
  5. Calculate Trend Intercept
    148.03 = 148.03

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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