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Time series classification deals with classifying the data points over the time based on its behavior. There can be data sets which behave in an abnormal manner when comparing with other data sets. Identifying unusual and anomalous time series is becoming increasingly common for organizations.
1:06 21:11 Suggested clip Math Tutorial: Patterns and Trends in Time Series Plots (statistics YouTubeStart of suggested client of suggested clip Math Tutorial: Patterns and Trends in Time Series Plots (statistics
Trend(T)- reflects the long-term progression of the series. Cyclic (C) reflects repeated but non-periodic fluctuations. Seasonal(S)-reflects seasonality present in the Time Series data, like demand for flip-flops, will be highest during summer.
Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation.
A trend is the general direction of a price over a period of time. A pattern is a set of data that follows a recognizable form, which analysts then attempt to find in the current data. Most traders trade in the direction of the trend.
Time series consist of four components: (1) Seasonal variations that repeat over a specific period such as a day, week, month, season, etc., (2) Trend variations that move up or down in a reasonably predictable pattern, (3) Cyclical variations that correspond with business or economic 'boom-bust' cycles or follow their
Data collected irregularly or only once are not time series. An observed time series can be decomposed into three components: the trend (long term direction), the seasonal (systematic, calendar related movements) and the irregular (unsystematic, short term fluctuations).
Definition: The trend is the component of a time series that represents variations of low frequency in a time series, the high and medium frequency fluctuations having been filtered out.
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