Income Fund Mutual Fund Forward View - Triple Exponential Smoothing

AMECX Fund  USD 26.97  0.07  0.26%   
The Triple Exponential Smoothing forecast reference data for Income Fund Of is based on the equity's recent trading history. This page summarizes the model output and key accuracy metrics for reference.
The Triple Exponential Smoothing forecasted value of Income Fund Of on the next trading day is expected to be 26.91 with a mean absolute deviation of 0.09 and the sum of the absolute errors of 5.31.As with simple exponential smoothing, in triple exponential smoothing models past Income Fund observations are given exponentially smaller weights as the observations get older. In other words, recent observations are given relatively more weight in forecasting than the older Income Fund Of observations. All Triple Exponential Smoothing forecast figures shown for Income Fund Of are reference data reflecting model output based on available historical prices.
Triple exponential smoothing for Income Fund - also known as the Winters method - is a refinement of the popular double exponential smoothing model with the addition of periodicity (seasonality) component. Simple exponential smoothing technique works best with data where there are no trend or seasonality components to the data. When Income Fund prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any trend in Income Fund price movement. However, neither of these exponential smoothing models address any seasonality of Income Fund.

Triple Exponential Smoothing Price Forecast For the 20th of March

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Income Fund Of on the next trading day is expected to be 26.91 with a mean absolute deviation of 0.09 , mean absolute percentage error of 0.02 , and the sum of the absolute errors of 5.31 .
Please note that although there have been many attempts to predict Income Mutual Fund prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Income Fund's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Mutual Fund Forecast Pattern

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Forecasted Value

For the next trading day, Macroaxis evaluates Income Fund's predictive range by looking for statistically meaningful downside and upside boundaries. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Market Value
26.97
26.91
Expected Value
27.37
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Triple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Income Fund mutual fund data series using in forecasting. Note that when a statistical model is used to represent Income Fund mutual fund, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.
AICAkaike Information CriteriaHuge
BiasArithmetic mean of the errors 0.0159
MADMean absolute deviation0.09
MAPEMean absolute percentage error0.0033
SAESum of the absolute errors5.3095
As with simple exponential smoothing, in triple exponential smoothing models past Income Fund observations are given exponentially smaller weights as the observations get older. In other words, recent observations are given relatively more weight in forecasting than the older Income Fund Of observations.

Other Forecasting Options for Income Fund

Whether a novice or experienced investor, anyone considering Income needs to understand the dynamics of Income Fund's price movement. Price charts for Income Mutual Fund contain a significant amount of noise that can distort investment decisions.

Income Fund Related Equities

The following equities are related to Income Fund within the Allocation--70% to 85% Equity space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing Income Fund against peers on metrics such as P/E, margins, and return on equity helps contextualize its positioning and identify relative strengths or weaknesses.
 Risk & Return  Correlation

Income Fund Market Strength Events

Analyzing market strength indicators for Income Fund enables investors to understand how the mutual fund performs relative to overall market momentum. These indicators are valuable tools for identifying when to enter or exit positions in Income Fund Of.

Income Fund Risk Indicators

Identifying and analyzing Income Fund's key risk indicators is a foundational step in projecting how its price may evolve. This process helps investors quantify the risk associated with Income Fund's and decide how to manage it.
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.

Story Coverage note for Income Fund

Story coverage around Income Fund Of often expands when market conditions, narrative momentum, or risk-adjusted performance make the security more visible to investors. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.

Other Macroaxis Stories

Macroaxis publishes story content for a diverse readership that includes finance students, independent investors, money managers, and market-focused operating teams. What connects that audience is a focus on building stronger portfolios through better research, risk awareness, and comparative analysis.