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Time Series Analysis in Data Mining

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Time Series Decomposition

Time series decomposition is a technique that breaks down a time series into trend, seasonality, and error components. This aids in understanding complex series by isolating simpler components.

Forecasting Models

Forecasting models are algorithms used to predict future values of a time series based on historical data. They are critical in planning and decision making for businesses and organizations.

Granger Causality Test

The Granger Causality Test is used to determine if one time series can forecast another. It doesn't test true causality, but rather if one series contains information that helps predict another.

Moving Average

A moving average is a technique used to smooth out short-term fluctuations and highlight longer-term trends or cycles. It calculates the average of the time series data over specific past periods.

Trend

The trend in a time series refers to a long-term increase or decrease in the data. Identifying trends helps in understanding the overall direction of the data over time.

Seasonality

Seasonality refers to periodic fluctuations in time series data that occur at regular intervals, such as daily, monthly, or quarterly. Identifying seasonality is important for making accurate predictions.

Stationarity

Stationarity in a time series data means that statistical properties like the mean, variance, and autocorrelation are constant over time. It’s vital for many statistical modeling techniques as they require the time series to be stationary.

Differencing

Differencing is a data transformation technique that computes the differences between consecutive or seasonal observations. It's used to stabilize the mean of a time series by removing changes in the level of a time series, thereby eliminating (or reducing) trend and seasonality.

Autocorrelation

Autocorrelation refers to the correlation of a time series with its own past and future values. It's essential for identifying repeating patterns such as seasonality.

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