Time Series Forecasting: Examples, Principles, and Applications

Time Series Forecasting: Examples, Principles, and Applications

Time Series Forecasting: Examples, Principles, and Applications

No matter whether you are a data science professional or not, you’ve probably come across time series data at some stage in your life. In short, it is just a dataset that tracks one point of data over a period of time, from weather information to stock prices or some such.  

This article explains about such data, how to analyze the datasets, and their applications, using examples.

What is Meant by Time Series Data?

The time series data or time-stamped data is recorded over regular periods of time. In short, any non-stationary value that is purely dependent on time can be part of a time series. 

Being a data science professional, you can make use of time series data to find out fundamental trends or causes of various patterns over a duration of time

This concept may seem familiar because you may have probably come across it in your day-to-day life, either knowingly or unknowingly. Whether you’ve used a calendar to track your working hours over the duration of a week, or tracked your house rent monthly over a year, you’ve successfully noted time series data. 

It is not necessary that time-stamped data should be numeric - it can be Boolean, string, float64, or int64. The added advantage is that it can be visualized in various kinds of charts to allow trend analysis, anomaly detection, and insight extraction. Grafana and InfluxDB are some of the popular tools for dashboarding, and also for time series visualization. 

What Are the Types of Time Series Analysis?

Listed below are the most types of time series analysis:

  • Forecasting
  • Descriptive analysis
  • Explanative analysis
  • Interrupted analysis
  • Regression analysis
  • Exploratory analysis
  • Association analysis
  • Classification
  • Segmentation
  • Curve fitting

Forecasting: Forecasting is used to make predictions based on the historical time series data. 

Descriptive Analysis: It is the powerful method used to find out different components in time series data. 

Explanative analysis: The goal of explanative analysis is to understand data, their relationships between data points, etc.

Interrupted analysis: Also called quasi-experimental analysis is mainly used to find out alternations in a time series analysis from before to after a set of external influences probably affecting the basic
variable.

Regression analysis: Regression analysis is used to check relationships between one or multiple time series. 

Exploratory analysis: Exploratory analysis shows major features found in time series data in a visual manner. 

Association analysis: The aim of association analysis is to find out associations between two features in the dataset. 

Classification: Classification is used to analyse, and allocate properties to time series data.  

Segmentation: Segmentation looks at assigned properties, and splits the data into segments as per assigned properties. 

Curve fitting: This model is useful to study the relationships of numerous variables within a given dataset by arranging variables along a curve. 

What Are the Main Components? 

Time series data has 4 major components:

  1. Trend: - It defines the long-term and constant downward or upward movement. It is not the same as seasonal variation because it is unpredictable and difficult to identify. A trend is said to be deterministic if are able to find the cause, and it is said to be stochastic if it is unexplainable. For example, if a company releases a new video on YouTube, and the video gets lots of views/subscribers, such a trend would be considered deterministic.
  2. Cycle: A cycle is unpredictable, and involves both up and down movement around a trend. It differs from seasonal variation because it doesn’t have an exact, and match time between time durations. 
  3. Seasonality: Fluctuations that arise at a fixed, and expected frequency. For example, there are increased sales in the summer season because the weather is hot.
  4. Irregularity: Irregularities are not fixed, and are random. A better example of irregular variations is changing in petrol prices.  

By finding out the above components within a dataset, it is possible to do adjustments and transformations. Out of all, seasonal adjustments will result in more faultless forecasting. 

What is Regular and Irregular Time Series?

Time series is said to be regular if the values are collected at equal and constant time periods. Whereas, the series is said to be irregular if the values are collected at unequal, irregular, and unexpected intervals. For example, Google Analytics records the session only when the user visits a website, or a radar gun calculates the speed of the ball as soon as the bowler bowls it. These two examples are enough to understand the concept of irregular time series data. 

Some simple examples of time series data 

Consider a train timetable. This is the best example of a time series use case. Here, the time series data model can speak for information, including the halt and arrival of trains. It is possible to aggregate over a period of time how many people travelled in a train, or which train line was busy at which location. 

Similarly, it is possible to analyze social media and define how repeatedly a hashtag in an hour is used, and how many replies this hashtag received during that period. 

Some other common examples of time series are: daily temperatures, the number of babies born in a month, or the number of people voted on a given day. 

Difference Between Time Series Data and Cross-sectional Data

It is easy to observe a time series as it has only two elements: a time period that is identical and clearly specified, and a single unit measurement tracked at the end of each of the time intervals. 
Cross-sectional data, on the other hand, includes various values tracked during a fixed or single duration of time. 

Here, the goal is a comparison of different entities. The stress is not on analyzing data over intervals for making forecasts.

Further, you can merge both cross-sectional and time series data. Such a fresh dataset is said to be panel data. This data could be helpful in tracking the impact of social advantages on unemployment over a duration of time. 

What Is Time Series Forecasting?

Time series forecasting is a collection of methods both in data science and statistics that looks at previous records to select a model for forecasting future observations. The use cases for this concept include sales and demand observations, scientific works related to global warming, among other things. 

Forecasting is of two types:

  • Univariate: Contains a single variable
  • Multivariate: Contains multiple variables  

Practical Examples of Time Series Forecasting

Following are the different examples of time series models:

  • Forecasting the winner of a game: You can use time series models to predict the game’s result, adding +1 if the person wins and -1 if the person loses. 
  • Estimating population: Most countries use time series data to forecast the population after every ten years.
  • Hypothesis testing: You can use time series models for predicting global temperatures, thinking of it as a real occurrence and analyzing if the predictions are right or wrong. 

How do you conduct a time series analysis?

You should follow the below-mentioned steps to conduct a time series analysis on a specific dataset:

  • Plot different graphs to analyze, and understand the data’s outliers, peaks, trends, and seasonality.
  • Use mathematical tools to analyze the chances of making data stationery
  • Choose a time series forecasting model based on the kind of data handled by you
  • Use inverse transformations to convert series back to its old form
  • Now, analyze the predictions 

What are the four principles of time series forecasting?

  1. Understand various time series patterns: It’s known the time series involves trends, seasonality, and cycles. Many people won’t understand the cyclic, and seasonal behavior properly. 
  2. Make use of features carefully: Make use of features with extra care when you are not sure about their future real patterns. In case, features are easily predictable or have values then you have to build a forecast model according to them. Utilizing expected values as features is risky because it leads to major errors and yields a biased outcome. 
  3. Be ready to handle smaller dataset: Smaller time series need to be dismissed. All time-oriented datasets are valuable in forecasting. A smaller dataset could be useful to analyze the whole dataset and produce plots that could be visualized easily. 
  4. Select the correct resolution: Having a perfect vision of the goals of your analysis will drive better outcomes. It can eliminate the chances of propagating the error to the total. So, selecting the correct resolution will reduce all such “noise” that makes modeling complex. 

Importance of Time Series Analysis

Time series analysis is a concept that mainly concentrates on understanding the dependencies in data as it’s not the same over time. 

By looking at the previous data, professionals can make smart decisions related to behavior across different sectors such as finance, retail, business, and real estate then use that data to make decisions regarding the future. 

Applications of Time Series

Here are some applications of time series:  

  1. Manufacturing and Production Companies: Digitalization of production plants, and manufacturing companies generate vast quantities of time series data. It’s obtained from sensors measuring temperature, pH, pressure, and velocity among other things.
  2. Financial sector: The banking and financial sectors use time series data to analyze and improve their services. For example, stock market professionals rely on algorithms related to time series to optimize their performance.
  3. Consumer electronics: Consumer electronics industry is growing day by day because people are dependent on electronic devices. Smart home systems use simple types of time series data to analyze and find out animals and animals, detect intruders, and so on.
  4. Healthcare: Medical practitioners use ECGs, and EEGs to deploy data for medical diagnosis. Further, it played a very important role during the COVID-19 pandemic as the global community received accurate, and timely data to keep updated with growing trends in COVID-19 statistics. 

In conclusion:

I hope this article has helped readers get a sound understanding of the basics of time series data. It is important for any business or data scientist to be aware of the real value of data. In the world of digital transformations, a business’ success depends on how it gathers, stores, and utilizes time series data.  

Standard (Image)