What Is Time Series Analysis? Exploring Definitions, Types, Techniques, and Applications
Time Series Analysis
What is Time Series Analysis
For every business, one of the most important metrics to evaluate and understand growth is - the number of sales done. On its own the number of sales done doesn't provide any statistical information, however, when the sales are analyzed for each month over a year, patterns that help understand the monthly growth can be identified which can lead to data-driven decision-making. Analyzing data points for each month's sales over a year is an example of time series analysis.
Time series analysis is the analysis of a sequence of data points that have been collected over a regular interval of time for a fixed period. This helps in identifying patterns within the data which in turn provides statistical insights.
Time series analysis is a crucial aspect of data analysis, adding time as a grain while collecting and analyzing data adds depth to the data, which helps in extracting insights from the data, and leveraging more information from it.
Multiple types of patterns are observed within the data when it’s observed over a period of time, some of which are discussed in the next topic - Time Series visualizations.
Time Series Visualizations
Before diving into the techniques of time series analysis, it's essential to understand the various patterns that can emerge when data is observed over time. These patterns provide a critical context for analysis.
Trends - As per the word’s definition - a general direction in which something is developing or changing. A trend exists when there is a long-term increase or decrease depicted in the data over a period.
Seasonal - Seasonality is when there is a regular fluctuation in data. For example, the sales of groceries are the highest in the first week of every month and then decrease gradually.
Cyclic - A cyclic pattern occurs when there is an irregular fluctuation in the data.
Once we can visualize the data, the next important step in data analysis is to extract useful information and forecast future values using it. Multiple techniques have been discovered and enhanced over time. A few of them are discussed in the next topic - Techniques of Time Series Analysis.
Techniques of Time Series Analysis
The primary use case of data analytics is to be able to generate useful insights from the data and also use the same for future forecasts. Following are the techniques that can be used to leverage the data gathered for time series analysis.
Moving Average - The moving average is a simple statistical method used for forecasting, majorly long-term trends. This method calculates the average of the last ‘n’ records. In the below grid, the running average of the sales is calculated by taking the average of the last 2 months.
Year
Month
Average users
Moving Average
2022
January
10.4
Nan
2022
February
11.5
Nan
2022
March
7.5
Nan
2022
April
6.8
9.8
2022
May
10.65
8.6
2022
June
11.45
8.316667
2022
July
6.2
9.633333
2022
August
7
9.433333
2022
September
10.22
8.216667
2022
October
11.3
7.806667
2022
November
6.2
9.506667
2022
December
7.9
9.24
2023
January
10.5
8.466667
2023
February
11.4
8.2
2023
March
6.3
9.933333
2023
April
7.2
Exponential Smoothing - This method is similar to the moving average method, but instead of giving equal weightage to all data points, this method, gives more weight over the recent data points, therefore giving more consideration to the recent data points.
ARIMA - ARIMA stands for Autoregressive Integrated Moving Average. ARIMA model uses past values to predict future values, this is why it is an autoregressive model. Integrated here means that the data is ‘stationary data’, the time series data is made stationary by subtracting the previous observations. Moving Average means that there is a linear relation between future data and previous data.
Incorporating Machine Learning: Machine learning methods have gained prominence in recent years. Techniques like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are being used to capture intricate dependencies within time series data. These deep learning approaches can be highly effective for tasks such as natural language processing, speech recognition, and even stock market prediction.
Hybrid Approaches: Combining classical time series analysis with machine learning methods is another exciting avenue for research and application. Hybrid models aim to harness the strengths of both worlds, utilizing the interpretability of traditional techniques and the predictive power of machine learning algorithms. For example, you can use ARIMA models to forecast a time series and then fine-tune the forecast with a machine learning model.
Application of Time Series Analysis
Time series analysis finds a wide range of applications across various industries. Here are some notable use cases:
Sales Forecasting - Sales forecasting is a crucial way via which businesses manage their operations and inventory. Time series analysis can help in estimating future sales using which businesses can make key decisions.
Traffic Management - Smart cities use time series data from traffic cameras and sensors to optimize traffic flow. This information helps in managing congestion, reducing commute times, and enhancing road safety.
Fraud Detection - The past data points linked to a bank account can be analyzed to study the normal frequency of transactions done each day over a period of time. Any irregular activities and spikes in transactions can then be linked to fraud.
Financial analysis - Stock market analysis for investors is very important before investing their money. This can be achieved by using time series analysis techniques on data points for any company’s stocks and then forecasting their growth.
Health Monitoring - In today’s day and age where we have smart watches to track heart rate, pulse, etc. Time series analysis can be used here to track any irregularities and then warn the user accordingly.
Supply Chain Management - Time series analysis aids supply chain management by predicting demand patterns for products. This allows businesses to stock the right quantities of products, minimize wastage, and ensure timely deliveries
Climate Change - Climate change can be predicted by time series analysis of data related to CO2 emission, using this predictive measure can be taken to build a better future for the planet.
Natural Disaster Prediction - Time series analysis plays a crucial role in predicting natural disasters like earthquakes, hurricanes, and floods. By monitoring geological and meteorological data over time, scientists can issue early warnings, potentially saving lives and reducing property damage.
Data analysis is an expanding field, every day we are being able to store more and more data, from various sources. A data analyst's work is to be able to extract relevant information from this data and leverage it in business development and problem-solving.
Conclusion
In conclusion, time series analysis is an indispensable tool for understanding, forecasting, and making data-driven decisions in various domains. By leveraging its techniques and visualizations, we can extract valuable insights from the temporal data and apply them to solve real-world problems and enhance our understanding of the past, present, and future.
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