# Start time series analysis – Florida News Times

From stock market analysis to economic forecasting, earthquake forecasting, industrial processes and quality control, time series analysis has a multitude of applications that businesses of all kinds rely on to detect trends, make forecasts and improve results. There is. Over the past year, the use of time series modeling to manage pandemic responses has been one of the most pressing applications of time series analysis.

Time series analysis identifies attributes of time series data such as trends and seasonality by measuring statistical characteristics such as covariance and autocorrelation. Once the attributes of the observed time series data are identified, they are interpreted and integrated with other data for use in anomaly detection, prediction, and other machine learning tasks.

Programming languages â€‹â€‹used for time series analysis and data science include Python, R, Java, and Flux.Learning The link between time series and data science Whether you want to become a data scientist or need to perform time series forecasting or anomaly detection for your use cases, this is a great place to start.

## Storage and visualization of time series data

Time series like the Internet of Things (IoT) play a major role in all of our lives and industrial IoT technology increasingly relies on time series analysis to achieve operational efficiency and enable predictive maintenance. Ability to ingest, store and analyze data chronologically Required within the data infrastructure.

A dedicated time series platform with a user interface and integrated analytics to capture and manage time series data greatly prepares organizations to process time series data and perform data modeling and business workloads. online machine learning. Useful.Ann Efficient dedicated time series database Users should automatically interrupt old data, easily down-sample the data to low-resolution data, and transform the time series on a schedule for future analysis.

Since time series analysis is based on data plotted over time, another need is to visualize the data (often in real time) to see patterns that may occur over time. Is to observe.Ann Efficient dedicated user interface Facilitates mutual collaboration with teams working on time series in different time zones, efficiently renders visualizations representing millions of time series points, and allows users to easily make corrections based on time series data. You have to be able to do it.

## Time series data attributes

Time series data can be understood through three components or characteristics:

**Trend**It denotes systematic change at the series level, i.e. long term direction. The direction and slope of the trend (rate of change) may remain constant or change throughout the series.**Seasonal**It refers to a series of repeated patterns of increase and decrease that occur consistently throughout the period. Seasonality is generally viewed as a periodic or repeating pattern over the course of a year, but the seasons are not limited to a one-year timescale. Seasons can also exist in the nanosecond range.**Residual**Find out what’s left after removing seasonality and data trends.

In time series, the independent variable is often time itself and is used to make predictions. To achieve this, we need to understand whether the time series is “stable” or seasonal.

A time series is stationary if it has a constant mean and variance, regardless of changes in the independent variables of time itself. Covariance is often used as a measure of the stationarity of a series. Autocorrelation is often used to identify seasonality in a time series. Autocorrelation measures the similarity of observations between a time series and the lag or lagged copy of that time series.

## Classic time series model

The first step in making a time series forecast is to learn about the different algorithms and methods that can help you achieve your goals. Always investigate the statistical assumptions underlying your algorithm of choice to see if your data violates those assumptions. Traditional time series forecasting models fall into three broad categories.

**Autoregressive model**It is used to represent the type of random process and is most often used to perform time series analysis in the context of economics, nature, and other fields. The predictions of autoregressive models depend linearly on past observations and probabilistic terms.**Moving average model**Predictions are commonly used to model univariate time series because they depend linearly on the residual error of previous predictions. It is assumed that the time series is stationary.**Exponential smoothing model**Used for univariate time series. The forecast is an exponentially weighted sum of past observations.

The attributes and use cases of time series data help you determine which time series forecasting model to use.

#### [ Also on InfoWorld: Visualizing time series data ]

## Time series analysis method

Different methods of time series analysis serve different purposes. For example:

**Spectral analysis**Widely used in fields such as geophysics, oceanography, atmospheric sciences, astronomy and engineering. This allows you to discover the periodicity behind your time series data. Spectral density can be estimated using an object called a periodogram. A periodogram is the square correlation between a time series and a sine / cosine wave at different frequencies across the time series.**Wavelet analysis**Used for signal processing. Wavelets are localized functions in time and frequency, generally with zero mean. It is also a tool for breaking down signals by location and frequency.

*Anais Dotis-Georgiou is an InfluxData developer advocate with a passion for beautifying data using data analytics, AI and machine learning. It takes the data it collects and applies a combination of research, research and engineering to transform the data into something functional, valuable and aesthetic. When not behind the screen, you can find her drawing, stretching, boarding, and chasing a soccer ball.*

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