What type of data are collected over several time periods?

Time series data is gathered, stored, visualized  and analyzed for various purposes across various domains:

  1. In data mining, pattern recognition and machine learning, time series analysis is used for clustering, classification, query by content, anomaly detection and forecasting.
  2. In signal processing, control engineering and communication engineering, time series data is used for signal detection and estimation.
  3. In statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the time series analysis is used for forecasting.

Time series data can be visualized in different types of charts to facilitate insight extraction, trend analysis, and anomaly detection. Time series visualization and dashboarding tools include the InfluxDB UI and Grafana.

The term 'time series patterns' describes long-term changes in the series. Whether measured as a trend, seasonal, or cyclic pattern, the correlation can be calculated in a number of ways (linear, exponential, etc.), and the direction may change at any given time.

Time series data is used in time series analysis (historical or real-time) and time series forecasting to detect and predict patterns — essentially looking at change over time. Following is a brief overview of each.

Time series analysis methods

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 set period of time, rather than intermittently or at random.

Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data. TSA helps identify trends, cycles, and seasonal variances to aid in the forecasting of a future event. Factors relevant to TSA include stationarity, seasonality and autocorrelation.

Time series analysis can be useful to see how a given variable changes over time (while time itself, in time series data, is often the independent variable). Time series analysis can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period.

Learn more about time series analysis methods, including spectral analysis, wavelet analysis, autocorrelation, and cross-correlation.

Time series forecasting methods

Time series forecasting uses information regarding historical values and associated patterns to predict future activity.

Time series forecasting methods include:

  • Trend analysis
  • Cyclical fluctuation analysis
  • Seasonal pattern analysis

As with all forecasting methods, success is not guaranteed. Machine learning is often used for this purpose. So are its classical predecessors: Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters.

To ‘see things’ ahead of time, time series modeling (a forecasting method based on time series data) involves working on time-based data (years, days, hours, minutes) to derive hidden insights that inform decision-making. Time series models are very useful models when you have serially correlated data. Most businesses work on time series data to analyze sales projections for the next year, website traffic, competitive positioning and much more.

Learn more about time series forecasting methods, including decompositional models, smoothing-based models, and models including seasonality.

What is sigma in statistics?

Since this is regarding statistics I assume you mean lower case sigma (σ) which, in statistics, is the symbol used for standard deviation, and σ2 is known as the variance.

What type of data are collected over several time periods?

Transcribed Image Text:Data collected over several time periods are called Select one: a. qualitative data b. cross-sectional data c. quantitative data d. time series data

What type of data are collected over several time periods?

What type of data are collected over several time periods?

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