Miscellaneous Topics - Time series, Business Mathematics and Statistics

# Miscellaneous Topics - Time series, Business Mathematics and Statistics - Business Mathematics and Statistics - B Com

Seasonal variation indices are calculated as below :
Seasonal variation index =

Meaning of “Normal” in Business Statistics
Business is often said to be “above normal” or “below normal”. When so used the term “normal” is generally recognized to mean a level of activity which is characterized by the presence of basic trend and seasonal variation. This implies that the influence of business cycles and erratic fluctuations on the level of activity is assumed to be insignificant. Therefore, the product of trend value for any period when adjusted by the seasonal index for that period gives us an estimate of the normal activity during that period.

Measuring Cycle as the residual
Business cyclical variations are measured either as the difference between the observed value and the “normal”. Whatever remains after elimination of secular trend and seasonal variations from the time series, is said to be composed of cyclical variations and Irregular movements.

Second degree Parabola
The simplest form of the non-linear trend is the second degree parabola. It is used to find long term trend. We use the following equation for finding second degree trend –
Yc = a + bX + cX2
To know the value of a, b and c we use the following three normal equations –
∑Y = Na + b∑X + c∑X2
∑XY = a∑X + b∑X2 + c∑X3
∑X2Y = a∑X2 + b∑X3 + c∑X4
A second degree trend equation is apporpriate for the secular trend component of a time series when the data do not fall in a straight line.

Illustration: Fit a parabola (Yc = a + bX + cX2) from the following
Years   1   2  3   4   5   6  7
Values 35 38 40 42 36 39 45

– 84c = – 4
c = 4/84 = 0.05
By substituting the value of c in equation (i) we get the value of a
7a + 28 × 4/48 = 275
7a = 275 – 1.33
a = 273.67/7 = 39.09

We may get the value of b with the help of equation (ii)
28b = 28
b = 1
The required equation would be:
Yc = 39.09 + 1X + 0.05 X2
= 39.09 + X + 0.05 X2
With the help of above equation we can estimate the value for year 8 where x = 4
Yc = 39.09 + 4 + 0.05 (4)2
= 39.09 + 4 + 0.8 = 43.89

Exponential Trend
The equation for exponential trend is of the form: y = abx
Taking log of both sides we get log y = log a + x log b
To get the value of a and b we have normal equation
∑logy = Nlog a + logb ∑X
∑(x. log y) = log a∑x + log b∑X2
When we slove these equations we get –
log a = and log b =

Illustration : The production of certain raw material by a company in lakh tons for the years 1996 to 2002 are given below:
Year :          1996 1997 1998 1999 2000 2001 2002
Production : 32      47    65    92    132  190   275
Estimate Production figure for the year 2003 using an equation of the form y = ab1 where x = years and y = production

Solution :

log y = 1.9704 + .154 x
for 2003, x would be 4 and log y will be
log y = 1.9704 + .154(4) = 2.5864
y = AL 2.5864 = 385.9
Thus estimated production for 2003 would be 385.9 lakh tons.

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## FAQs on Miscellaneous Topics - Time series, Business Mathematics and Statistics - Business Mathematics and Statistics - B Com

 1. What is a time series? Ans. A time series is a sequence of data points collected and recorded at regular intervals over a period of time. It is used to study the behavior and patterns of the data over time, making it a valuable tool in various fields such as economics, finance, and meteorology.
 2. How is time series analysis used in business mathematics? Ans. Time series analysis is extensively used in business mathematics to analyze historical data and make predictions about future trends. It helps businesses identify patterns, seasonality, and trends in their sales, production, or other key metrics. By understanding these patterns, businesses can make informed decisions regarding resource allocation, inventory management, and sales forecasting.
 3. What are the different components of a time series? Ans. A time series typically consists of four main components: trend, seasonality, cyclical variations, and random or irregular fluctuations. The trend represents the long-term movement or direction of the data, seasonality refers to regular patterns that repeat within a fixed period, cyclical variations are longer-term fluctuations not related to a specific season, and random or irregular fluctuations are unpredictable and often caused by unforeseen events or noise in the data.
 4. How is statistical modeling applied to time series analysis? Ans. Statistical modeling is used in time series analysis to capture and describe the patterns and relationships within the data. Techniques like autoregressive integrated moving average (ARIMA) models, exponential smoothing, and regression analysis are commonly employed. These models help in forecasting future values, identifying outliers or anomalies, and understanding the impact of different factors on the time series data.
 5. What are some common challenges in time series analysis? Ans. Time series analysis can present several challenges, such as dealing with missing or irregularly spaced data, handling outliers or extreme values, and choosing the appropriate model for forecasting. Additionally, time series data often exhibit non-stationarity, meaning that the statistical properties of the data change over time, requiring techniques like differencing or transformation. It is also crucial to consider the limitations and assumptions of the chosen model to ensure accurate analysis and predictions.

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