Introduction to Time Series
In today’s modern and digital world statisticians are pretty much occupied with analyzing the patterns of the consumer. We are generating a huge amount of data and this data should be simply just trashed. There is a tremendous amount of value to the data that is being generated and if processed properly then it can gain fortunes to the organization by preparing it to the mindset of its consumers. Whether we want to assess the electricity consumption pattern of the consumers or to study the statistics behind the financial trends in the market time analysis plays a crucial role.
In the modern world where there is a huge importance to technological research and booming digital technology time is an important factor that needs to be considered. In order to predict consumer usage analysis which can be his financial investments or his electricity consumption or his expenditure on e-commerce or predicting the positive growing stocks in the future and the planning, the investment, etc time series plays a crucial role.
In definition terms, time-series is generally a series of ordered points on the timeline with time being always the independent variable and with the aim to predict the future trend.
The data gathered is expected to be of one of the three below mentioned types:
- Time Series Data: This is nothing but the noted or observational values that are taken at different time frames.
- Cross-Sectional Data: Data from one or more dependent variables collected at the same given time.
- Pooled Data: This is hybrid data which can be the combination of data and cross-sectional data.
Mathematically the time-series can be obtained by the below equation:
y=f(t) with t being the independent variable time and y is generally the response to the time over the function.
There are other major aspects:
- Stationary: If a function depending on variable time is said to be stationary and if the statistical properties of that function do not change with time. In other words, we are supposed to have constant mean and variance with stationary characterized.
- Seasonality: The periodic fluctuation over time is referred to as seasonality with its correlations with seasons in a year. The best example of these characteristics is the electricity in residential areas where the consumption fluctuates between night and day. During the night we see max consumption by the households and day time the consumption is reduced considerably. Thus if we draw a graph then we see that depending on the period the peak of the function varies accordingly.
- Autocorrelation: Autocorrelation is the similarity between two or more observations with a time lag between them. This also gives significant ideas for time-series pattern analysis.
We have seen the characteristics above and it is possible that we have a graph in which there can be a combination of the above characteristics in that graph.
Why do we Need Time Series?
- A series of events indexed based on time is Time Series.
- They are mostly plotted using line graphs or line charts.
- To answer why we need time series we need to know the vast area where they are implemented and this list will quite large as prediction is becoming one of the major influencing factors for the organizations to garnish their consumers.
- It has its fundamentals in statistics and probability and hence it is widely employed by statisticians.
- It is also employed for the digital signal process where often we see time as one of the independent variables.
- Pattern recognition basing one of some predefined characteristics is one of the applications where it has identified its presence. Also, time series is vastly employed by mathematicians, in the study of econometrics.
- It has founded its application in earthquake detection, estimating impacted areas during the prediction of natural calamities, and also in understanding the weather patterns over a period.
- Apart from the above-mentioned fields it also has found its application in astronomy, control engineering, electromagnetics. Thus time series analysis has become one of the staples for science and engineering technological fields.
Importance of Time Series
Given below are some of the importance mentioned:
- It is used by many organizations to forecast their business profit or loss trends and thus important business decisions can be taken for development.
- It is used to compare the present trend with the past trend that has already happened so the future trend can be estimated and prepared.
- The cycle variations over a period using time series will allow us to understand the business cycle quite effectively.
- It is used to understand the correlated seasonal trends of the data.
- It is also used to understand how an event can change its feature over a period of time and hence the reliability, flexibility, and other important features can be predicated.
- It is also used in the quality control process where the quality trend is predicated over time.
- If we receive the complex signal pattern for it then we can apply some transformations such as Fourier analysis to denoise the graph and break the complex pattern into a series of simpler patterns and hence a better understanding can be achieved.
Thus we can see that time series is the dataset that has patterns with significant impact over the time frame. These patterns may or may not be repeated multiple times. This article gives answers to the most voted question “how can we better get the idea or predict the future” so that we can be prepared. This pattern can be seasonal or stationary or correlated.
This is a guide to What is Time Series? Here we discuss the introduction, why we need time series? and importance respectively. You may also have a look at the following articles to learn more –