Time series analysis is mainly used to explain, describe, and predict changes via the time of chosen variables. Many companies use time series forecasting, and analysis to develop business strategies. These techniques help businesses in measuring, tracking, defining, and predicting business trends. Time series analysis provides you with an opportunity to know the impact of the past over the future.
What is meant by Time Series?
Time series is a sequence of data points spread over a specific duration of time, where time is the independent variable and other variables are not constant. The time-series data is analyzed over a regular temporal interval.
Time series data can be helpful in the following cases:
- Tracking weather data on an hourly, daily, and weekly basis
- Tracking the performance of websites
- Predicting earthquakes
The time series has 4 variations like
- Seasonal variations
- Trend variations
- Cyclical variations, and
- Random variations
What is Time Series Analysis?
Time series analysis is the process of determining the basic patterns disposed by the data over a duration of time. This approach isn’t expensive and is used to forecast business-related metrics including sales, turnover, analysis of the stock market, and budgetary analysis.
Applications of Time Series Analysis
- Time series in the financial domain: Most financial investments and decisions related to businesses are taken into account based on future changes in the financial domain. After analyzing financial data, an expert can easily predict necessary forecasts for financial applications in many areas including risk evolution, portfolio construction, and so on. Time series analysis has become part of financial analysis and is used to predict foreign currency risk, interest rates, etc. Financial forecasting analysis is used by business experts, and policymakers to make better decisions related to production, purchases, and allocation of resources.
- Time series in the medical industry: Medicine has developed as a data-driven field and constantly contributing to time series analysis with advancements. In the medical sector, one shouldn’t forget to examine the change of behaviour overtime on absolute values.
- Time series in astronomy domain: Several areas of astronomy and astrophysics needs the help of time series. However, the astronomy domain comprises arranging objects, faultless measurements, and trajectories, so astronomical experts use time series analysis to study objects as per their choices. Time series data had a remarkable impact on understanding, and measuring everything related to the universe.
- Time series forecasting in business development: Businesses can make informed decisions with the help of time series forecasting, as the process includes analysis of past data patterns.
It is also helpful in forecasting upcoming potentials, and events in the below mentioned ways:
- Reliability: Time series forecasting is reliable when the data displays a broad time period like huge numbers of observations for a longer period of time.
- Trend evaluation: Time series techniques can be conducted to identify trends, for instance, these techniques check data observations to find out when measurements show an increase or decrease in a sale of a specific product.
- Seasonal patterns: Variations of recorded data points reveal seasonal patterns and fluctuations that consider as a platform for data forecasting. The received details are important for markets whose products vary seasonally and help businesses in product development planning, and delivery requirements.
- Growth: Time series technique is helpful in measuring endogenous, and financial growth.
Time series analysis and its applications are useful in controlling problems in businesses. As it involves the understanding of past behaviour to predict the future, which is very important for business planning. It also helps in the evaluation of present achievements.
Let’s look at the top 5-time series analysis tactics for business development:
- Deep learning for Time series analysis: Time series forecasting isn’t simple when working with noisy data, many inputs and output variables, long sequences, and multi-step forecasts. The different time-series capabilities offered by deep learning methods include automatic learning, temporal dependence, and automatic handling of temporal structures.
- Time-series data analysis: By gathering data at various points in time, it’s possible to do time-series data analysis. This is when compared to the cross-sectional data that notices businesses at a single point in time.
- Autocorrelation and Seasonality: Autocorrelation defines the level of similarity between the provided time series, and its delayed type over a period of time. This time series indicates a set of values of an entity. It helps identify the relationship between the present values, and the past values of a variable/entity. Seasonality is one of the foremost characteristics of time series data. It takes place if the time series displays predictable, and continuous patterns at time periods that are no larger than a year. The perfect example of this is retail sales that are at a peak level between September to December and weak between January and February.
- Time series analysis in R: Data miners and statisticians use an R programming language to develop data analysis. It consists of a series of libraries specially designed for data science. R offers remarkable features to communicate the discoveries with documentation tools that make it simpler to explain analysis to the whole team. It offers both qualities, and formal equations for time series models like white noise, autoregression, easy moving average, and random walk. You can find many types of R functions for time series data such as simulating, forecasting, and modelling trends. R is the perfect business forecasting tool and is best for doing time series analysis.
- Time series regression: This statistical technique is used for guessing a future response by considering the old response history called autoregressive dynamic. Predictors use the time series regression method to guess the behaviour of dynamic systems from experimental data.
Three goals such as predicting, modelling, and characterization can be achieved with the help of regression analysis. The prime objective plays a vital role in achieving these three goals. At times, modelling is simple to predict better, and otherwise, it is enough to know and explain what’s happening. Most of the time, the repetitive method is used in predicting, and modelling.
What are the Benefits of Using Deep Learning to Analyze Time Series?
The use of deep learning for time series analysis involves the following benefits:
- Trouble-free extract features: Deep neural networks reduce the necessity of the data scaling process and stationary data and feature engineering procedures which are necessary for time-series forecasting. These neural networks can learn by themselves, if proper training is provided, they can extract features from the raw input data without any difficulty.
- Not difficult to predict from training data: Data can be represented at several points in time using many deep learning models including gradient booster regressor, random forest, and time-delay neural networks.
Advantages of Time Series Analysis:
- The time-series data will lead to arduous, and complicated calculations because of its nature which in turn makes forecasting difficult. With the help of Python and R languages, analysts can create and tune perfect time series forecasts with minimal effort.
- Time series models have fewer assumptions and are stable. It means in case a large and unpredicted event occurs; the model can provide valuable insight to solutions throughout the event.
Disadvantages:
- The major disadvantage is that time series analysis is expensive because forecasts are derived from historical data patterns that are necessary to predict the upcoming market behaviour.