Time Series Data Mining
Time series represents a collection of values or data obtained from the logical order of measurement over time. Time series data mining makes our natural ability to visualize the shape of real-time data. It is an ordered sequence of data points at uniform time intervals.
Time Series Analysis comprises methods for analyzing time-series data in order to extract meaningful statistics, rules and patterns. These rules and patterns might be used to build forecasting models that are able to predict future developments.
Is the database play a vital role in Time Series mining?
The database is the collection of data retrieved from a different source in which the data are stored in a structural, nonstructural format on their respective columns.
Time Series database consists of a sequence of values or events changing with time. Data are recorded at regular intervals.
Application of Time Series Mining:
1. Financial:
1.1 Used for stock price evaluation
1.2 For the measurement of Inflation
2. Industry:
2.1 Determine the power consumption
3. Scientific:
3.1 Used for experiment results
4. Meteorological:
4.1 Concerned with the processes and phenomena of the atmosphere, basically for forecasting weather
Characteristic of time series components:
1. Trend
2. Cycle
3.Seasonal
4. Irregular
Category of Time-Series Movements:
1. Long-term or trend movements :
The general direction in which a time series is moving over a long interval of time. It shows the general tendency of the data to increase or decrease a long period of time.
2. Cyclic movements or cycle variations:
Long term oscillations about a trend line or curve. For example, business cycles. This oscillatory movement has a period of oscillation of more than a year.
3. Seasonal movements or seasonal variations:
Almost identical patterns that a time series appears to follow during corresponding months of successive years. This variation will be present in a time series if the data are recorded hourly, daily, weekly or monthly.
4. Irregular or random movements:
These fluctuations are unforeseen, uncontrollable and unpredictable. They are not regular variations and are purely random or irregular.
