Time series
A time series is a series of data points that occur in consecutive order over a period of time. A time series tracks the movement of selected data points (such as the price of a security) over a specified period of time.
Applications of time series span various industries such as: observing electrical activity in the brain, measuring precipitation, forecasting stock prices, tracking annual retail sales, monthly subscribers, heart rate per minute,…
Time series data is a collection of observations obtained through repeated measurements over time. Time series data is everywhere, because time is part of everything we can perceive.
Components of the time series
A time series data is usually decomposed into the following four subcomponents:
- Trend: This component indicates the overall trend of the data over time: up or down, up or down. For example, inflation tends to increase the average price.
- Seasonality: component indicating trends by season, month, quarter,… For example:
– Naturally occurring events, like weather fluctuations
– Business or administrative procedures, such as the start or end of a financial year.
– Calendar events, such as the number of Mondays each month or holidays, change from year to year. - Cycle: the cyclical component, it differs from the seasonal element in that this component has a movement over a longer period of time (many years).
- Irregular remainder: also known as white noise, the noise component that remains after extracting all the components above, it indicates the anomaly of the data points.
Instead of making predictions on the original data, which is a very bumpy and unpredictable line of sight, we do it on regular looking sub-components with more obvious movement trends and then combine the components. this together. Cycle shows the trend of movement over a long period of time (usually the cycle falls in 7 years or more), because our data is not spread wide enough, we will only be interested in the remaining 3 components. are Trend, Seasonality and Irregular remainder.
Example of time series data
Observe the chart of stock price changes below:
Another familiar example of time series data is monitoring a patient’s health, such as in an electrocardiogram (ECG), which monitors the heart’s activity to indicate whether it is working properly. .
In addition to being logged at regular intervals, time series data can be logged whenever a certain event occurs, such as logging in programming. Logs will record events, processes, messages, and communication between software applications and operating systems.
How are time series data used?
Time series data is collected, stored, visualized and analyzed for different purposes across different domains:
- In data mining, pattern recognition, and machine learning, time series analysis is used for clustering, classification, content query, anomaly detection, and prediction operations.
- In signal processing, control engineering, and communication engineering, time series data is used for signal detection and estimation.
- In statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics, time series analysis is used for forecasting.
Time series data can be visualized in different chart types to facilitate insights mining, trend analysis, and anomaly detection.
Time series data is used in time series analysis (historical or real time) and time series forecasting to detect and predict patterns. The following is a brief overview of each type.
Time series analysis
Time series analysis is a method of analyzing a series of data points collected over a period of time. In time series analysis, data points are recorded at regular intervals over a given period, rather than intermittently or randomly.
Time series analysis is the use of statistical methods to analyze data; extract meaningful statistics and characteristics about the data. This helps identify trends, cycles, and seasonal differences to aid in predicting a future event.
To learn more about time series analysis, you can refer here: https://machinelearningcoban.com/tabml_book/ch_data_processing/timeseries_data.html
Time series forecasting
Time series forecasting uses information regarding historical values and related patterns to predict future activity.
For forecasting, time series modeling involves working on time data to derive knowledge that helps in decision making. Time series models are very useful models when we have correlated time data.
Time series forecasting will be covered in detail in the next article.