What is Time Series analysis?
History and Definition
Time Series is a sequence of well-defined data points measured at consistent time intervals over a period of time. Data collected on an ad-hoc basis or irregularly does not form a time series. Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data.
Time series Analysis helps us understand what are the underlying forces leading to a particular trend in the time series data points and helps us in forecasting and monitoring the data points by fitting appropriate models to it.
Historically speaking, time series analysis has been around for centuries and its evidence can be seen it the field of astronomy where it was used to study the movements of the planets and the sun in ancient ages. Today, it is used in practically every sphere around us – from day to day business issues (say monthly sales of a product or daily closing value of NASDAQ) to complicated scientific research and studies (evolution or seasonal changes).
Benefits and Applications of Time Series Analysis
Time series analysis aims to achieve various objectives and the tools and models used vary accordingly. The various types of time series analysis include –
- Descriptive analysis – to determine the trend or pattern in a time series using graphs or other tools. This helps us identify cyclic patterns, overall trends, turning points and outliers.
- Spectral analysis – is also referred to as frequency domain and aims to separate periodic or cyclical components in a time series. For example, identifying cyclical changes in sales of a product.
- Forecasting – used extensively in business forecasting, budgeting, etc based on historical trends
- Intervention analysis – is used to determine if an event can lead to a change in the time series, for example, an employee’s level of performance has improved or not after an intervention in the form of training – to determine the effectiveness of the training program.
- Explanative analysis – studies the cross correlation or relationship between two time series and the dependence of one on another. For example the study of employee turnover data and employee training data to determine if there is any dependence of employee training programs on employee turnover rates over time.
The biggest advantage of using time series analysis is that it can be used to understand the past as well as predict the future. Further, time series analysis is based on past data plotted against time which is rather readily available in most areas of study.
For instance, a financial services provider may want to predict future gold price movements for its clients. It can use historically available data to conduct Time series analysis and forecast the gold rates for a certain future period.
There are various other practical applications of time series analysis including economic forecasting, census analysis and yield projections. Further, it is used by investment analysts and consultants for stock market analysis and portfolio management. Business managers use time series analysis on a regular basis for sales forecasting, budgetary analysis, inventory management and quality control.
Utility of Time Series AnalysisThe analysis of Time Series is of great significance not only to the economist and businessman but also to the scientist, geologist, biologist, research worker, etc., for the reasons given below:
(1) It helps in understanding past behaviors.By observing data over a period of time one can easily understanding what changes have taken place in the past, Such analysis will be extremely helpful in producing future behavior.
(2) It helps in planning future operations.Plans for the future cannot be made without forecasting events and relationship they will have. Statistical techniques have been evolved which enable time series to be analyzed in such a way that the influences which have determined the form of that series to be analyzed in such a way that the influences which have determined the form of that series may be ascertained. If the regularity of occurrence of any feature over a sufficient long period could be clearly established then, within limits, prediction of probable future variations would become possible.
(3) It helps in evaluating current accomplishments.The performance can be compared with the expected performance and the cause of variation analyzed. For example, if expected sale for 1995 was 10,000 refrigerators and the actual sale was only 9,000, one can investigate the cause for the shortfall in achievement. Time series analysis will enable us to apply the scientific procedure of “holding other things constant” as we examine one variable at a time. For example, if we know how much the effect of seasonality on business is we may devise ways and means of ironing out the seasonal influence or decreasing it by producing commodities with complementary seasons.
(4) It facilitates comparison.Different time series are often compared and important conclusions drawn there from.
However, one should not be led to believe that by time series analysis one can foretell with 100percnet accuracy the course of future events. After all, statisticians are not foretellers. This could be possible only if the influence of the various forces which affect these series such as climate, customs and traditions, growth and decline factors and the complex forces which proclimate, customs and traditions, growth and decline factors and the complex forces which produce business cycles would have been regular in their operation. However, the facts of life reveal that this type of regularity does not exist. But this then does not mean that time series analysis is of value. When such analysis is couples with a careful examination of current business indicators once can undoubtedly improve substantially upon guest mates (i.e., estimates based upon pure guesswork) in forecasting future business conditions.
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