Updated May 20, 2023
Introduction to Time Series
In today’s modern and digital world, statisticians are pretty much occupied with analyzing consumer patterns. We are generating a huge amount of data, which should be simply trashed. There is a tremendous amount of value to the data generated. If processed correctly, it can gain fortunes for the organization by preparing it to the mindset of its consumers. Whether we want to assess the consumers’ electricity consumption pattern or 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 on technological research and booming digital technology, time is an essential factor that needs to be considered. To predict consumer usage analysis which can be financial investments, electricity consumption, 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, a time series is generally a series of ordered points on the timeline, with time always being the independent variable to predict the future trend.
The data can be from one of the three types:
- Time Series Data: This is nothing but the noted or observational values 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 a 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.
Other Major Aspects:
- Stationary: If a function depending on variable time is said to be stationary and the statistical properties of that function do not change with time. In other words, we are supposed to have a constant mean and variance with stationary characteristics.
- Seasonality: The periodic fluctuation over time is a seasonality with its correlations with seasons in a year. The best example of these characteristics is the electricity in residential areas where consumption fluctuates between night and day. We see maximum household consumption at night, and in the daytime, consumption reduces considerably. Thus, if we draw a graph, we see that depending on the period, the function’s peak 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 we may 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. This list will be extensive as prediction is becoming a major influencing factor for organizations to garnish their consumers.
- It has its fundamentals in statistics and probability, so statisticians widely employ it.
- It is also helpful for the digital signal processor, where we often 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 helpful for mathematicians in econometrics.
- It has founded its application in earthquake detection, estimating impacted areas during the prediction of natural calamities, and understanding weather patterns over time.
- Apart from the abovementioned fields, it also has found its application in astronomy, control engineering, and electromagnetics. Thus, time series analysis has become one of the staples for science and engineering technological fields.
Importance of Time Series
Some of the Importance are mentioned below:
- It is helpful for many organizations to forecast their business profit or loss trends. Thus essential business decisions can facilitate development.
- It is helpful 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 helpful to understand the correlated seasonal trends of the data.
- It is also helpful in the quality control process to predicate the quality trend over time.
- Suppose we receive the complex signal pattern for it. In that case, we can apply transformations, such as Fourier analysis, to denoise the graph and break the complex pattern into simpler patterns. Hence, a better understanding can be achieved.
- It is also helpful to understand how an event can change its feature over a period of time. Hence, reliability, flexibility, and other essential features can be predicated.
Conclusion
Thus we can see that time series is the dataset with patterns with significant impact over time. These patterns may or may not repeat multiple times. This article answers the most voted question, “How can we get better the idea or predict the future?” so that we can be prepared. This pattern can be seasonal or stationary, or correlated.
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