Temporal Data Analysis Task

A Temporal Data Analysis Task is a sequential data analysis task that is restricted to temporal data.

• AKA: Time-Series Mining/Modeling.
• Context:
  • It can range from being a Temporal Summarization Task to being a Temporal Prediction Task.
  • It can range from being a Qualitative Temporal Data Analysis Task to being a Quantitative Temporal Data Analysis Task.
  • It can range from being a Heuristic Temporal Data Analysis Task to being a Data-Driven Temporal Data Analysis Task.
  • It can range from being a Univariate Temporal Data Analysis Task to being a Multivariate Temporal Data Analysis Task.
  • It can range from being a Temporal Data Point Analysis Task to being a Temporal Motif Analysis Task.
  • It can be solved by a Temporal Data Analysis System (that implements a temporal analysis algorithm).
• Example(s):
  • a Temporal Detrending Task.
  • a Time Series Future Point Prediction Task, such as timeseries future point classification and timeseries future point estimation.
  • a Temporal Pattern Evolution Analysis Task.
  • a Time Series Clustering Task.
  • a Data Stream Mining Task.
  • a Time Series Time-Interval Estimation Task, such as a Survival Analysis Task (with a time-to-event dataset).
  • a Time Series Forecasting Task, common in Meteorology.
  • a Time Series Outlier Detection Task.
  • …
• Counter-Example(s):
  • a Graph Data Analysis Task.
  • a Spatial Data Analysis Task.
• See: Signal Processing, Quantitative Finance, Seismology, Geophysics, Signal Processing, Control Engineering, Communication Engineering.

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References

• http://www.stats.gla.ac.uk/steps/glossary/time_series.html

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/time_series#Motivation Retrieved:2015-10-11.
  • In the context of statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the primary goal of time series analysis is forecasting. In the context of signal processing, control engineering and communication engineering it is used for signal detection and estimation, while in the context of data mining, pattern recognition and machine learning time series analysis can be used for clustering, classification, query by content, anomaly detection as well as forecasting.
• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/time_series#Analysis Retrieved:2015-10-11.
  • There are several types of motivation and data analysis available for time series which are appropriate for different purposes.

2010

• (Koren, 2010) ⇒ Yehuda Koren. (2010). “Collaborative Filtering with Temporal Dynamics.” In: Communications of the ACM, 53(4). doi:10.1145/1721654.1721677
  • Modeling time drifting data is a central problem in data mining.

2002

• (Keogh & Kasetty, 2002) ⇒ Eamonn Keogh, and Shruti Kasetty. (2002). “On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration.” In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/775047.775062
  • For concreteness we begin by defining the various tasks that occupy the attention of most time series data mining research.
    • Indexing (Query by Content): Given a query time series Q, and some similarity/dissimilarity measure D(Q,C), find the nearest matching time series in database DB.
    • Clustering: Find natural groupings of the time series in database DB under some similarity/dissimilarity measure D(Q,C).
    • Classification: Given an unlabeled time series Q, assign it to one of two or more predefined classes.
    • Segmentation: Given a time series Q containing n datapoints, construct a model Q, from K piecewise segments (K << n) such that Q closely approximates Q.

2001

• (Brillinger, 2001) ⇒ David R. Brillinger. (2001). “Time Series: data analysis and theory." SIAM. ISBN:0898715016

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2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Time_series Retrieved:2017-10-29.
  • A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

    Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting , container shipping freight rate forecasting ,earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called "time series analysis", which focuses on comparing values of a single time series or multiple dependent time series at different points in time. Interrupted time series analysis is the analysis of interventions on a single time series Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.) Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language ).