What makes Time Series Important in Data Science?

Time series is one of the unique data types used in data science. In most cases, this type of data represents a sequence of observations over time. Time-series analysis is widely applied to understand trends and patterns in large sets of heterogeneous data from various domains such as economics, meteorology, medicine, etc. In this article, we will discuss some reasons that make it special.

The term “time-series” refers to a data set in which a particular event or variable is measured repeatedly over successive, adjacent units of time. Time-series analysis is used for understanding patterns, relationships, and trends in data. It can be applied to a wide range of different data types such as financial, weather, seismology and even Twitter feeds. Time-series analysis often involves processing multiple sequences (i.e., time series) one after the other or on overlapping periods of time. For example, we could divide a given year into 12 monthly segments and perform some sort of analysis on each month separately (such as to see how temperature changes throughout the year). On the other hand, we could do the opposite; that is, analyze all those segments at once but with different variables (to discover any correlations between and humidity).

In this field there are three types of techniques that are widely used:

- ARIMA (Autoregressive Integrated Moving Average) models — Exponential Smoothing.

- Autocorrelation function(ACF), Partial autocorrelation Function(PACF) plots

- Autoregressive Distributed Lags (ADL).

These techniques allow analyzing different properties of the time series such as:

- Trend Detection (stationary, nonstationary)

- Seasonality detection and identification (periodic/nonperiodic), seasonal shift and drift, and first and second-order differencing.

- Forecasting using different methods like ARMA-GARCH; TAR models; NZM. It should be noted that time series forecasting can also be done by neural networks. In fact, it is a very well explored area in deep learning since there are many scenarios where it could be useful to apply deep learning for this kind of problem. The last work from Google on Stock Market prediction is one good example.

In addition, these techniques have been applied in different fields outside data mining, such as medicine and finance.

- Clustering: this task consists of grouping a set of data points so that data points in the same group (called a cluster) are more similar to each other than those from different clusters. It is usually used for finding hidden patterns and structures within the time series. For instance, it can be useful for finding seasonality or periodic components within a time series; outlier detection; forecasting. Moreover, clustering techniques have been successfully applied to discover new knowledge like protein function or gene functions.

- Modeling: it consists in fitting a time series with an appropriate model to make predictions about future values. The model we choose should not only fit well with our data but also result in meaningful forecasts and avoid overfitting the data. The autoregressive integrated moving average (ARIMA) model is commonly used for this purpose.

- Dimension reduction: it consists in mapping multidimensional data into a smaller number of dimensions to facilitate visualizing it or performing further statistical analysis on it. Principal component analysis and factor analysis are both used for this objective.

- Visualization: from a given time series, we can extract its main characteristics by means of suitable visualization techniques such as line chart, phase diagram, etc. It also facilitates identifying relations between different variables which will be useful when clustering or modeling them. For instance, geographical maps have been widely used to discover links between diseases and environmental factors.

Time series analysis helps us understand the medium-term prospects for economic growth. It provides a mechanism to analyze the relationship between two variables that vary with respect to time, and it enables forecasting. The field of time series analysis includes several techniques that can classify or cluster samples into groups based on their similarities. More recently, deep learning methods have been applied to problems in time series processing where complex nonlinear relationships are involved. Other models developed from statistical methodologies like ARMA can be used to generate forecasts from historical data.

As already mentioned above, time-series has become immensely popular because it enables us to understand the medium-term prospects for economic growth. Time series can also provide valuable insight into your business environment and help you identify patterns such as seasonality (repeated fluctuations within a particular time range) which can help you forecast future performance. A time series is a sequence of data points indexed (or listed or graphed) in non-decreasing order of some parameter — typically time, or space. Many real-world processes are best modeled by time series.

Originally published at https://protonautoml.com on August 9, 2021.