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In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis , as described in Trend estimation .
Data patterns, or trends, occur when the information gathered tends to increase or decrease over time or is influenced by changes in an external factor. Linear trend estimation essentially creates a straight line on a graph of data that models the general direction that the data is heading.
In time series data, seasonality refers to the trends that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays [1] and consists of periodic, repetitive, and generally regular and predictable patterns in the levels [2] of a time series.
Sheryl Connelly is the head of Ford's Global Trends and Futuring Division, where she separates trend from fad and helps the auto maker determine what global changes will influence the market in ...
Many of the patterns follow as mathematically logical consequences of these assumptions. One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing. Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend.
The Elliott wave principle, or Elliott wave theory, is a form of technical analysis that helps financial traders analyze market cycles and forecast market trends by identifying extremes in investor psychology and price levels, such as highs and lows, by looking for patterns in prices.
Predictive modeling is a statistical technique used to predict future behavior. It utilizes predictive models to analyze a relationship between a specific unit in a given sample and one or more features of the unit. The objective of these models is to assess the possibility that a unit in another sample will display the same pattern.
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.