Historically speaking, time series analysis has been almost for ages, and its proof can be seen in the area of astronomy where it was applied to investigate the changes of the planets and the sun in ancient times. Today, time series forecasting is used in almost every field around us. For instance, in academics, time series analysis is used to determine patterns of data collected over time or forecast future events in data analysis.
So, what exactly do you mean by forecasting?
Forecasting is the process of predicting future events based on old data. The old data is used in systematic in order to forecast future events. It is the estimation of the future that is not subject to subjective considerations. However, the quality of forecasts management can be strongly related to the information that can be extracted and used from past data.
Components in time series forecasting
A time series is affected by four components. These components are trend, seasonal, cyclical and irregular components.
TRENDS : These generally relate to long term changes, whether linearly or non linearly in data. The changes can be linearly sharp such as price increase or decline in market share. The below example of a decreasing linear trend :
2. SEASONALITY : These are periodic changes that get impacted on the particular time frame. There are repetitive variations in time-series which may occur due to buying patterns and social habits of the customers during a year.
3. REGULAR VARIATIONS: These variations are due to fluctuations in data which are not being monitored or due to analysis which weren’t performed detailed manner but can be taken care of in order to have valid explanations for erratic fluctuations. Such fluctuations are a result of a variety of factors such as sudden weather changes or some clashes. As these variations are truly random, their occurrence in the future will have an impact on sales.
4. CYCLICAL VARIATION: This type of variation arises due to the phenomenon of business cycles. The business cycle attributes to the periods of expansion followed by periods of contraction.The business cycle may vary yearly according to cyclic variations. The duration and the level of sales may vary depending on the nature of the business that is quite difficult to predict.
Considering these above components, the effect can be calculated using two different types of models are generally used for a time series viz. multiplicative and additive models.
Multiplicative model can be represented by an equation: Y(t) = t(t)× s(t)×c(t)× i(t)
Additive model is represented by an equation: Y(t) =t(t) + s(t) + c(t) + i(t)
Y(t) = observation at time t
t(t) =the trend component
s(t) = the seasonal component
c(t)= the cyclic component
In Multiplicative model assumes that the four components of time series may or may not be independent, and they might affect one another. In additive model, we assume that all the four components are independent of each other.
Need for forecasting
1. Purpose – Any activity conceived in the present to deal with some possibility of gathering out of a circumstance or set of conditions set in the future. These future conditions offer a reason/focus to be accomplished in order to exploit or to limit the effect of these future conditions. Therefore, poor forecasts may lead to poor planning, thereby increasing the operational cost of the company. Hence, it's important to forecast the future with the best ability, experience and Judgment.
2. Time – To get ready an arrangement, to sort out assets for its usage, to execute; and complete the arrangement; all these need time as an asset. A few circumstances need next to no time, while some need quite a long time. Thus, if the future gauge is accessible ahead of time, proper activities can be arranged and executed in-time.
Applications of forecasting
Estimating Economic Trends
With the conceivable exemption of offers determining, the broadest estimating exertion is dedicated to anticipating financial patterns on a territorial, national, or even global level.
Estimating Staffing Needs
For monetarily created nations there is moving accentuation from assembling to administrations. Products are being delivered outside the nation (where work is part) and after that imported. Simultaneously, an expanding number of business firms have some expertise in giving an administration or the like (e.g., travel, the travel industry, excitement, lawful guide, wellbeing administrations, monetary, instructive, plan, upkeep, and so on.). For such an organization gauging "deals" progresses toward becoming anticipating the interest for administrations, which at that point converts into determining staffing needs to give those administrations.