Time Series is a series of data points ordered in time. In mathematics, time series is a sequence taken at successive equally spaced points in time. In simple words, it is a sequence of discrete time data. Time series tracks the movement of the chosen data points over a specified period of time with data points recorded at regular intervals.
According to Mooris Hamburg “A time series is a set of statistical observations arranged in chronological order”.
Uses of Time Series:
It is used for prediction or to detect the changes in patterns in collected data. Here are few uses of time series mentioned below:
· Used to predict future values
· Evaluation of current achievements
· Identify the changes in economics and business
· Pattern recognition
· Weather forecasting
· Earthquake prediction
· Signal Processing
· Statistics and Mathematical Finance
Benefits of Time Series
- Time series analysis helps in identifying patterns: Identifying patterns can reduce error.
- Creates the opportunity to clean your data
- Forecasting can help in predicting the future
- Allows to see what factors influence certain variables from period to period
- Helps to compare the performance of two different series for same time duration
- Helps to compare the performance of present series with past series
- Assistance in management as timely patterns of data are analyzed in business= metrics
Limitations: Time Series analysis has some weakness like difficulty in obtaining appropriate measures, problems with accurately identifying the correct model to represent the data and the major limitation is that either linear or polynomial trends can lead to unrealistic forecasts.
Types of Time Series
1. Classification: It can be useful across many domains and applications. In this, categories of data are assigned.
2. Curve Fitting: In this, the relationship of variables of data can be studied by plotting data along a curve.
3. Descriptive Analysis: It identifies patterns in time series data.
4. Explanative Analysis: This type of time series understands data and relationships within it.
5. Exploratory Analysis: Studies main features of the data, in a visual format.
6. Forecasting: It is used to predict the future. In this, historic data is studied to predict the future.
7. Intervention Analysis: Measure changes of an event, how can it affect the data.
8. Segmentation: In segmentation, data is divided into segments to show the properties of the source information.
Components of Time Series:
Time Series include four components, these are:
1. Secular Trend: It describes the movement along the term.
2. Seasonal Variations: It defines seasonal changes.
3. Cyclical Fluctuations: It is related to period or periodical variations not seasonal variations.
4. Irregular Variations: It is related to random sources of variations of series.
Application of Time Series in Business:
Time series is used to determine a good model which can be used to forecast business metrics. It allows management to understand timely patterns in data and analyse trends in business. Here are following uses of time series in several business:
- Economic Forecasting
- Sales Forecasting
- Budgetary Analysis
- Stock Market Analysis
- Yield Projection
- Process and Quality Control
- Inventory Studies
- Workload Projections
- Census Analysis
- Utility Studies
1. Financial and Business Domain: Many financial sectors, businesses like banking, telecom, retails etc. decisions are implemented on the basis of future changes. Time series analysis has become the core part in financial analysis and useful to observe economic changes and other business related to changes over time.
2. Medical Domain: In the medical domain, it is important to examine the transformation of behaviour over time. Time series analysis helps in analysing human knowledge with developments or human behaviour. Time series applications have also extended their way into medicine and medical instruments.
3. Astronomy Domain: Time series has made a core impact on measuring anything in the universe. Data from astronomy have resulted in much research, due to which time series roles have been expanded in detecting astronomical objects.
4. Forecasting Weather: With passage of time, weather forecasts have reached beyond general weather conditions. In order to conduct atmospheric measurements, many highly functional devices are used and are interconnected to accumulate weather data at different geographical locations and at every bit of time as per requirement.
5. Business Development: Time Series help many businesses to make better decisions. As in it, past data patterns are studied to predict the future. As past data is studied, it also studies seasonal fluctuations, trend estimation etc., which make its forecasting reliable. It led to growth of any organization.
Time Series Analysis, Forecasting and Techniques application have become much essence and have increased in practical examples of real-life and a variety of research fields including business, economics, engineering, politics and many other fields. Time series is a technique which helps in predicting and forecasting future data on the basis of past data. It is also an important element of data science, it shows variables change over a certain period of time.