Consumer or market trend analysis can be categorized into three types: geographic, which is analyzing trends within a group that is defined by their geographic location; temporal, or analyzing trends over a specific period of time; and, intuitive, or analyzing trends based on demographic and behavioral patterns and/or
These four components are:
- Secular trend, which describe the movement along the term;
- Seasonal variations, which represent seasonal changes;
- Cyclical fluctuations, which correspond to periodical but not seasonal variations;
- Irregular variations, which are other nonrandom sources of variations of series.
- Graphical Method. Under this method the values of a time series are plotted on a graph paper by taking time variable on the X-axis and the values variable on the Y-axis.
- Semi-Average Method.
- Moving Averages Method.
- Method of least squares.
A time series is a sequence of numerical data points in successive order. In investing, a time series tracks the movement of the chosen data points, such as a security's price, over a specified period of time with data points recorded at regular intervals.
Seasonal variation is variation in a time series within one year that is repeated more or less regularly. Seasonal variation may be caused by the temperature, rainfall, public holidays, cycles of seasons or holidays.
Measurements of Trends: Method of Semi-Averages
- Method of Semi-Averages.
- In this method, the semi-averages are calculated to find out the trend values.
- Procedure:
- (i) The data is divided into two equal parts.
- (ii) The average of each part is calculated, thus we get two points.
- (iii) Each point is plotted at the mid-point (year) of each half.
The secular trend forms one of the four basic components of the time series. It describes the movement over the long term of a time series that globally can be increasing, decreasing, or stable. The secular trend can be linear or not.
In statistics, a moving average is a calculation used to analyze data points by creating a series of averages of different subsets of the full data set. By calculating the moving average, the impacts of random, short-term fluctuations on the price of a stock over a specified time-frame are mitigated.
- Pick time period (number of years)
- Pick season period (month, quarter)
- Calculate average price for season.
- Calculate average price over time.
- Divide season average by over time average price x 100.
Seasonal variation is measured in terms of an index, called a seasonal index. It is an average that can be used to compare an actual observation relative to what it would be if there were no seasonal variation. An index value is attached to each period of the time series within a year.
WHAT ARE SEASONAL EFFECTS? A seasonal effect is a systematic and calendar related effect. Some examples include the sharp escalation in most Retail series which occurs around December in response to the Christmas period, or an increase in water consumption in summer due to warmer weather.
Seasonal variation is a component of a time series which is defined as the repetitive and predictable movement around the trend line in one year or less. It is detected by measuring the quantity of interest for small time intervals, such as days, weeks, months or quarters.
We call these averages “seasonal factors.” To seasonally adjust your data, divide each data point by the seasonal factor for its month. If January's average ratio is 0.85, it means that January runs about 15 percent below normal.
A seasonal index is a measure of how a particular season through some cycle compares with the average season of that cycle. By deseasonalizing data, we're removing seasonal fluctuations, or patterns in the data, to predict or approximate future data values. Seasonal indices.
A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. For example, it is often used in technical analysis of financial data, like stock prices, returns or trading volumes.
Seasonality refers to predictable changes that occur over a one-year period in a business or economy based on the seasons including calendar or commercial seasons. One example of a seasonal measure is retail sales, which typically sees higher spending during the fourth quarter of the calendar year.
Earth and the sunThe cycle of seasons is caused by Earth's tilt toward the sun. The planet rotates around an (invisible) axis. At other locations in Earth's annual journey, the axis is not tilted toward or away from the sun. During these times of the year, the hemispheres experience spring and autumn.
Applies exponential smoothing twice, similar to double exponential smoothing. However, the trend component curve is damped (flattens over time) instead of being linear. This method is best for data with a trend but no seasonality.
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. Traders who go opposite the trend are called contrarian investors.
Definitions. A seasonal pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week). Seasonality is always of a fixed and known period. A cyclic pattern exists when data exhibit rises and falls that are not of fixed period.
A regularly recurring pattern, e.g., of seasonal fluctuation in prevalence of insect vectors or respiratory infections in primary school children. From: cyclical trend in A Dictionary of Public Health »
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. Time series forecasting is the use of a model to predict future values based on previously observed values.
Preliminary detection
- De-trend your data with a centered moving average the size of your estimated seasonality.
- Isolate the seasonal component with one moving average per relevant time-step (e.g. one moving average per calendar day for a weekly seasonality, or one per month for an annual seasonality).
Trend: The increasing or decreasing value in the series. Seasonality: The repeating short-term cycle in the series. Noise: The random variation in the series.
