Time Series

Time series refers to a chain of data points observed and recorded in a time order over a specific period. It represents the output obtained from monitoring and tracking specific events or processes.

It is also known as time-stamped data and plays a major role in analysis and forecasting processes. It involves noting measurements at equally spaced time intervals. Its construction helps academics study how a variable changes over time. For instance, it can be applied to study the price movements of a security over time.

• Time series refers to a chain of data points observed due to monitoring and recording in a time order over a specific period. 
• Its components are the secular trend, seasonal trend, cyclical variations, and irregular variations.
• Its analysis derives meaningful statistics, interprets trends, identifies patterns, and contributes to decision making. Examples of its application include budgetary analysis and stock market analysis.
• Its applications include various models generated to forecast data and induce strong strategic decision-making. Examples include weather forecasting and sales forecasting.

Time series contains observation in the numerical form represented in chronological order. Analysis of this observed data and applying it as input to derive possible future developments was popularized in the late 20th century. It was primarily due to the textbook on time series analysis written by George E.P. Box and Gwilym M. Jenkins. They introduced the procedure to develop forecasts using the input based on the data points in the order of time, famously known as Box-Jenkins Analysis.

It can be categorized into different types; one is the categorization into non-stationary and stationary time series. If stationary, it has stochastic properties like variance unvarying with time. Whereas for non-stationary, its properties vary with time, and it can be a trend, random occurrences, seasons, cycles, etc. It is easy and effective to model when it is stationary by applying statistical modeling methods. 

Various programming languages are used in the data analysis process involving time-dependent data. For example, its analysis with Python and R programming. It can also check if the data presented is stationary or non-stationary, and time-series databases are good for working on time-dependent data.

Secular Trend

It indicates the long-running pattern identified from the chain of data recorded. It can be increasing or decreasing, indicating the future direction. Although it is commonly known as an average tendency of any aspect, the trend may vary in specific parts oscillating between upward and downward. Still, the overall trend will depict a single movement only, either upward or downward. For example, in summer, the temperature may rise or decline in a day, but the overall trend during the first two months will show how the heat has been rising from the beginning.

Seasonal Trend

Seasonal variations represent the presence of rhythmic patterns. Certain pattern repeatedly occurs at the same period or point every year. For example, the sale of umbrellas increases during the rainy season, and air conditioners increase during summer. Apart from natural occurrences, man-made conventions like fashion, marriage season, festivals, etc., play a key role in contributing to seasonal trends.

Cyclical Variations

It represents a cyclical pattern composed of up and down movement. It may span more than one year and go from phase to phase to complete a cycle. A business cycle is a significant example of a cyclic variation, denoted that a business goes through four stages in its life. Starting from the introduction, expansion, prosperity, and decline. How well the company can perform and stretch its phases depends on its performance.

Irregular Variations

It refers to variations that are uncontrollable and inevitable. It occurs randomly, opposite to regular changes or occurrences, and does not associate with a pattern. These fluctuations are unpredictable and unexplainable. Forces like natural and man-made disasters can trigger irregular variations.

Analysis of time-stamped data constructed helps derive meaningful statistics and contributes to decision making. For example, it is used to understand and interpret trends and patterns. In addition, it helps in prediction, classification, segmentation, descriptive and intervention analysis, etc.

Its application is generally seen with non-stationary data to observe how certain things change over time. Examples include its vital application in the stock market, forex historical dataset, sales, inventory, and weather analysis. Hence expertise in it is important among the professionals from fields like retail, financial market, sales, weather forecasting, etc.

Time series forecasting means assessing the time-stamped data using statistical calculations and modeling to make predictions and induce strong strategic decision-making. The process is widely adopted in many sectors, for example, sales forecasting and weather forecasting. Forecasting highly depends on the nature of the data, and the process is usually performed on historical data. The more simplified it is, the more accurate the forecasting becomes.

The model does have its limitations as it does not guarantee accuracy and may vary from the actual outcome. However, the analysts have some operating authority over models to regulate such constraints. Examples of time series methods used for forecasting are Autoregression (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA).

This has been a Guide to Time Series and its Definition. We explain the time-series database, its data analysis & forecasting models, examples, & components. You can learn more from the following articles –

• Economic Forecasting
• Forecasting Methods
• Cointegration