Time series analysis
Time series analysis is a mathematical technique used to study and analyze patterns of data points over time for the purpose of forecasting the future. It attempts to understand the structure and factors behind the data being observed in order to choose an appropriate forecast model that can lead to better decision making. Time series is applied in many areas such as:
- Sales forecasting
- Economic forecasting
- Stock market analysis
- Budgetary analysis
- Process and quality control
- Yield projections
- Workload projections
- Inventory studies
- Census analysis
- Utility studies, and many more
Examples of time series data
Time series analysis is used in a wide variety of disciplines, which means, there is a wide variety of time series data. Here are a few examples based on the different fields that time series analysis is used today:
- Consumer price index
- Gross domestic product
- Unemployment rates
- Political indicators
- Migration data
- Mortality rates
- Disease rates
- Mosquito populations
- Weight tracking
- Blood pressure tracking
- Heart rate monitoring
- Cholesterol measurements
- Monthly sunspot observations
- Global temperatures
- Pollution levels
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Components of time series analysis
The components of time series analysis are simply the forces or the factors that influence the values of a study in a time series. They can be classified into three categories:
- Trend: This is the tendency of data to increase or decrease within a given period of time. It is usually a smooth average tendency. However, the increase or decrease is not always necessarily in the same direction; the tendencies may increase, decrease, or do nothing in different periods of time.
- Seasonal variations: These are forces that operate on periodic or regular manner over a given period of time, mostly over a span of less than twelve months. They have the same pattern throughout the period being studied and are mostly observed if the data is recorded daily, weekly, or monthly. Seasonal variations often result from man-made or natural forces. Various atmospheric conditions play a significant role in seasonal variations. For example, the sale of raincoats and umbrella depend on the rainy season and that of electric fans shoots up during summer. Similarly, man-made conventions such as festivals, habits, fashions, customs, and community occasions largely affect time series data. They happen every year and there can be an upswing in sales during these events.
- Cyclic variations: The cyclic variations occur over a duration of more than twelve months. Usually they emerge throughout the business cycle. Basically, businesses have a four-phase cycle comprising of success, recession, depression and recovery.The increase or decrease of revenue during these phases can be affected by economic and market factors, all of which fall under cyclic variations. Time series analysis enables businesses to discover trends and patterns in each phase and prepare effectively for what’s to come in the next phase.
Importance of time series analysis
Time series analysis is important in examining events over time. It has the following importance in data manipulation:
- Predict future patterns: Time series is one of the most effective ways to know what will happen in the future. Since it analyses data in a series of periods, we can easily predict future trends. For instance, a times series analysis may show that the demand for umbrellas increases during the rainy season and hence we can be able to plan effectively in order to meet this demand in the future.
- Estimate trends: Business managers can use the trends observed in data to plan for the future. For instance, if the data shows an increase in the number of customers who buy an item in a given period of time, the management can be able to devise ways to increase sales just by analyzing this data carefully.
- Increase growth: Time series analysis also helps in measuring financial growth. It may help businesses identify areas that they have spent more than necessary over the years and strategize ways to cut costs in the future. Analyzing past data may also help in improving productivity, which may result in economic growth.
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