5 Applications of Time Series Analysis

Introduction

 

As long as we are accumulating data, “time” remains a crucial factor. Essentially, in time series analysis, time is a significant element of data.

 

A time series is a collection of time-order observations of a specific process where the time interval between each observation is constant, say, weeks, months and years, in some cases small deviations in time intervals are acceptable. Time series data is helpful to investigate how an asset, security or an economic variable of interest changes over time. 

 

Time series analysis

 

At its simplest, a time series analysis is a process of analyzing an observation of data points collected over a period of time, i.e time series data. In time series analysis,  data analysts record data observations in constant intervals for a set of time periods instead of recording data observations randomly. The rate of observation (time interval) can be from milliseconds to several years.

 

In order to inspect “how variables change over time”, a time series data describes the phenomenon under inspection over specific points of time to analyze fluctuations in variables over time. The parameters of interest can vary across domains such as;

 

• Values recorded by scientific instruments per day

• Number of hits at some websites on a daily basis

• Weekly share values on a stock market

• Number of rainy days per year

 

Additionally, time series analysis deals with a large number of data points to ensure coherence and reliability. A massive amount of data reflects a good sample size that assures any discovered trend or pattern are not outliers. Also, the time series data can be used for forecasting- predicting future outcomes based on prior data. 

 

(Must check: Exponential smoothing tutorial)

 

Time series forecasting

 

Time series analysis assists companies in perceiving causes of fluctuations in trends or essential patterns over time, by using various data visualization techniques, organizations could study seasonal trends and research more to understand the causes of these trends. When organizations analyze time series data over regular intervals, they are using time series forecasting to anticipate upcoming events.

 

In simple terms, time series forecasting is a technique to anticipate future events by analyzing past data behaviour or trends while accounting for the assumptions that upcoming trends will hold similarities with past trends.

 

“Prediction is truly very difficult, especially if it’s about the unknown future”– Nils Bohr

 

In forecasting time series data, the objective is to predict how the data observation will continue or vary into the future. Forecasting methods that use time series data are used in fundamental analysis and technical analysis.

 

 

Applications of Time Series Analysis

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Applications of time series analysis 

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1. Time series in Financial and Business Domain

 

Most financial, investment and business decisions are taken into consideration on the basis of future changes and demands forecasts in the financial domain. 

 

Time series analysis and forecasting essential processes for explaining the dynamic and influential behaviour of financial markets. Via examining financial data, an expert can predict required forecasts for important financial applications in several areas such as risk evolution, option pricing & trading, portfolio construction, etc.

 

For example, time series analysis has become the intrinsic part of financial analysis and can be used in predicting interest rates, foreign currency risk, volatility in stock markets and many more. Policymakers and business experts use financial forecasting to make decisions about production, purchases, market sustainability, allocation of resources, etc.

 

In investment, this analysis is employed to track the price fluctuations and price of a security over time. For instance, the price of a security can be recorded; 

 

• For the short term, such as the observation per hour for a business day, and

• For the long term, such as observation at the month end for five years.

 

Time series analysis is extremely useful to observe how a given asset, security, or economic variable behaves/changes over time. For example, it can be deployed to evaluate how the underlying changes associated with some data observation behave after shifting to other data observations in the same time period.

 

 

2. Time series in Medical Domain

 

Medicine has evolved as a data-driven field and continues to contribute in time series analysis to human knowledge with enormous developments. 

 

Case study

 

Consider the case of combining time series with a medical method CBR (case-based reasoning) and data mining, these synergies are essential as the pre-processing for feature mining from time series data and can be useful to study the progress of patients over time.  

 

In the medical domain, it is important to examine the transformation of behaviour over time as compared to derive inferences depending on the absolute values in the time series. For example, to diagnose heart rate variability in occurrence with respiration based on the sensor readings is the characteristic illustration of connecting time series with case-based monitoring.

 

However, time series in the context of the epidemiology domain has emerged very recently and incrementally as time series analysis approaches demand recordkeeping systems such that records should be connected over time and collected precisely at regular intervals.

 

As soon as the government has placed sufficient scientific instruments to accumulate good and lengthy temporal data, healthcare applications using time series analysis have resulted in huge prognostication for the industry as well as for individuals’ health diagnoses.

 

Medical Instruments

 

Time series analysis has made its way into medicine with the advent of medical devices such as 

 

• Electrocardiograms (ECGs), invented in 1901: For diagnosing cardiac conditions by recording the electrical pulses passing through the heart.

• Electroencephalogram (EEG), invented in 1924: For measuring electrical activity/impulses in the brain.

 

These inventions made more opportunities for medical practitioners to deploy time series for medical diagnosis.

 

With the advent of wearable sensors and smart electronic healthcare devices, now persons can take regular measurements automatically with minimal inputs, resulting in a good collection of longitudinal medical data for both sick and healthy individuals consistently. 

 

(Also check: IoT in healthcare)

 

 

3. Time Series in Astronomy

 

One of the contemporary and modern applications where time series plays a significant role are different areas of astronomy and astrophysics, 

  

Being specific in its domain, astronomy hugely relies on plotting objects, trajectories and accurate measurements, and due to the same, astronomical experts are proficient in time series in calibrating instruments and studying objects of their interest. 

 

Time series data had an intrinsic impact on knowing and measuring anything about the universe, it has a long history in the astronomy domain, for example, sunspot time series were recorded in China in 800 BC, which made sunspot data collection as well-recorded natural phenomena.

 

Similarly, in past centuries, time series analysis was used 

 

• To discover variable stars that are used to surmise stellar distances, and

• To observe transitory events such as supernovae to understand the mechanism of the changing of the universe with time.

 

Such mechanisms are the results of constant monitoring of live streaming of time series data depending upon the wavelengths and intensities of light that allows astronomers to catch events as they are occurring.

 

In the last few decades, data-driven astronomy introduced novel areas of research as astroinformatics and astrostatistics; these paradigms involve major disciplines such as statistics, data mining, machine learning and computational intelligence. And here, the role of time series analysis would be detecting and classifying astronomical objects swiftly along with the characterization of novel phenomena independently.

 

 

4. Time series in Forecasting Weather

 

Anciently, the Greek philosopher Aristotle researched weather phenomena with the idea to identify causes and effects in weather changes. Later on, scientists started to accumulate weather-related data using the instrument “barometer” to compute the state of atmospheric conditions, they recorded weather-related data on intervals of hourly or daily basis and kept them in different locations. 

 

With the time, customized weather forecasts began printed in newspapers and later on with the advancement in technology, currently forecasts are beyond the general weather conditions. 

 

In order to conduct atmospheric measurements with computational methods for fast compilations, many governments have established thousands of weather forecasting stations around the world.

 

These stations are equipped with highly functional devices and are interconnected with each other to accumulate weather data at different geographical locations and forecast weather conditions at every bit of time as per requirements.

 

(Recommended blog: Weather Forecasting with big data)

 

 

5. Time series in Business Development

 

Time series forecasting helps businesses to make informed business decisions, as the process analyzes past data patterns it can be useful in forecasting future possibilities and events in the following ways;

 

• Reliability: When the data incorporates a broad spectrum of time intervals in the form of massive observations for a longer time period, time series forecasting is highly reliable. It provides elucidate information by exploiting data observations at various time intervals.

• Growth: In order to evaluate the overall financial performance and growth as well as endogenous, time series is the most suitable asset. Basically, endogenous growth is the progress within organizations’ internal human capital resulting in economic growth. For example, studying the impact of any policy variables can be manifested by applying time series forecasting.

• Trend estimation: Time series methods can be conducted to discover trends, for example, these methods inspect data observations to identify when measurements reflect a decrease or increase in sales of a particular product.

• Seasonal patterns: Recorded data points variances could unveil seasonal patterns & fluctuations that act as a base for data forecasting. The obtained information is significant for markets whose products fluctuate seasonally and assist organizations in planning product development and delivery requirements.

 

Conclusion

 

In general, time series are characteristics of complex datasets recorded over a constant period of time. Time series analysis, modelling and forecasting have much essence in practical examples of real-life and in a variety of research fields including business, economics, medical, astronomy, engineering, politics and many more. 

 

In this blog, we have understood several aspects of time series and discussed common applications of time series analysis.