## The Forecasting Arrays

The Forecast Arrays provides a graphical representation of several independent computer forecasting models:

- Composite
- Composite II
- Empirical
- Long-Term
- Trading Cycle
- Most Active High
- Alpha Cycle
- Most Active Low
- Beta Cycle
- Direction Change
- Panic Cycle
- Volatility
- Arrays Display Probability Distributions

The Forecast Array enables you to quickly see when the computer models are looking for ideal highs or lows and important changes in trends and volatility. The model’s bar increase when it’s targeting that specific time period where a turning point in price or volatility should unfold.

With the exception of the Trading Cycle indicator, each model is designed only to provide an indication of when the market will change trend at a specific point in time. Turning points in price (high or low) unfold on both the highest and lowest bars. There is no direct relationship between turning points and highs and lows in the array. A low in price may unfold with the highest plot and a high could form on the lowest plot or vice versa.

#### Composite

The Composite model is the aggregate of all of the computer models, which provides a good perspective of important dates ahead. Each separate model from empirical to volatility is taken as a sum and reflected in this model.

#### Composite II

The Composite II model represents a longitudinal timing model, which expand and contract through time. The cyclical frequencies are based upon the computer model’s interpretation of the market’s cyclical pattern.

#### Empirical

The Empirical model represents the transverse timing model, which is comprised of fixed frequencies. The frequencies are of fixed durations, which have been determined manually through years of research. The frequencies are unique to each market.

#### Long-Term

The Long-Term model represents a long-term transverse timing mode, which has a fixed frequency that is generally three-times that of the Empirical model. For instance, if the Empirical model represents a frequency that occurs every 16 weeks then the Long-Term model will represent a frequency of approximately 48 weeks. The frequencies are unique to each market.

#### Trading Cycle

The Trading Timing Model offers a union of time and direction that enables the end-user determine when a high or low is likely to occur, but it’s not assured as cycles can be subjected to destructive interference under the superposition principle.

Bullish and bearish markets have empirical nominal durations that last specific time units (days, weeks, months years):

- Bullish: 7-11-14-21 time units
- Bearish: 2-3-5-6-10-12-18 time units

Bullish trading cycles are measured from a low and Bearish trading cycles are measured from a high. The Trading Cycles model counts the bullish and bearish predictions that fall on a particular time unit. For instance the chart below shows how the cycles are counted (for brevity time units 21 & 18 were ignored)

Below is a sample of how the Trading Cycles data array will appear based on the data above:

#### Most Active Highs

The Most Active High model represents the transverse frequencies, which most often signal that a turning point is in place. These frequencies are generated exclusively from highs-to-highs and are different for each market.

#### Alpha Cycle

The Alpha Cycle model represents the analysis of transverse frequencies, which are generated from highs-to-highs. The data array bar increases or decreases in size depending on the number of independent frequencies that converge on same time interval. The frequency of the alpha cycle is different for each market.

#### Most Active Lows

The Most Active Low model represents the transverse frequencies, which most often signal that a turning point is in place. These frequencies are generated exclusively from lows-to-lows and are different for each market.

#### Beta Cycle

The Beta Cycle model represents the analysis of transverse frequencies, which are generated from lows-to-lows. The data array bar increases or decreases in size depending on the number of independent frequencies that converge on same time interval. The frequency of the beta cycle is different for each market.

#### Directional Change

The Directional Change model represents when a market will begin to make a decisive move. A Directional Change differs from a turning point in that the Directional Change target does not need to be the actual high or low.

During periods of high volatility it will be more common to find the Turning Point and Directional Change converge during the same time period. This normally occurs when a market is making a spike low or high.

#### Panic Cycle

The Panic Cycle model represents whether an abrupt move is about to occur within the market. A Panic Cycle differs from a Turning Point or a Directional Change insofar as it reflects neither a high nor a low and it is not the beginning of a change in trend.

Instead, a Panic Cycle more often than not is an outside reversals or just a capitulation. The model reflects greater price movement that can be dramatic in one direction or an outside reversal exceeding the previous session high and penetrating its low.

#### Volatility

The Volatility Models provides an indication as to when a change in the current volatility trend will take place. Unlike timing, volatility is only concerned with percentage movement and not the direction or whether a high or low has formed within the market. The model reflects "turning points" but in volatility. Thus, the low in volatility might form on the highest bar while the high in volatility could unfold on the lowest bar.

#### Arrays Display Probability Distributions

Forecast Arrays are a graphical representation of the probabilities of turning points occurring. They show the number of cycles converging on a particular time unit, which can be defined by days, weeks, months, quarters, or years. So if we are looking at a weekly Forecast Array, each day of the week is calculated as its own time unit to form the whole. Different models predict and calculate when a cycle will fall on a particular time unit to create a "hit". The hits are summed and compared to the hits of the other 12 time units. The unit with the largest number of hits within a specific time unit (i.e. days, weeks, etc.) will display as the largest bar and the other time units will display as a percentage of the largest bar.