MAINGATE MLP Mutual Fund Forward View - Triple Exponential Smoothing

IMLPX Fund  USD 11.68  0.21  1.83%   
This Triple Exponential Smoothing reference page for Maingate Mlp Fund presents model-generated forecast data based on historical daily prices. The output values and deviation metrics are provided for informational reference.
The Triple Exponential Smoothing forecasted value of Maingate Mlp Fund on the next trading day is expected to be 11.71 with a mean absolute deviation of 0.07 and the sum of the absolute errors of 4.23.As with simple exponential smoothing, in triple exponential smoothing models past MAINGATE MLP 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 Maingate Mlp Fund observations. All Triple Exponential Smoothing forecast figures shown for Maingate Mlp Fund are reference data reflecting model output based on available historical prices.
Triple exponential smoothing for MAINGATE MLP - 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 MAINGATE MLP 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 MAINGATE MLP price movement. However, neither of these exponential smoothing models address any seasonality of Maingate Mlp.

Triple Exponential Smoothing Price Forecast For the 21st of March

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Maingate Mlp Fund on the next trading day is expected to be 11.71 with a mean absolute deviation of 0.07 , mean absolute percentage error of 0.01 , and the sum of the absolute errors of 4.23 .
Please note that although there have been many attempts to predict MAINGATE 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 MAINGATE MLP'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

The next-day forecast for Maingate Mlp Fund focuses on identifying predictive downside and upside bands that can frame a realistic trading range. At the moment, the model places downside around 10.91 and upside around 12.52 for the forecasting period.
Market Value
11.68
11.71
Expected Value
12.52
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 MAINGATE MLP mutual fund data series using in forecasting. Note that when a statistical model is used to represent MAINGATE MLP 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 0.0141
MADMean absolute deviation0.0704
MAPEMean absolute percentage error0.0066
SAESum of the absolute errors4.2263
As with simple exponential smoothing, in triple exponential smoothing models past MAINGATE MLP 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 Maingate Mlp Fund observations.

Other Forecasting Options for MAINGATE MLP

Price movement is the most fundamental factor that determines whether MAINGATE is a viable investment for any investor. MAINGATE Mutual Fund price charts are often noisy, making it difficult to identify meaningful patterns without analytical tools.

MAINGATE MLP Related Equities

The following equities are related to MAINGATE MLP within the Energy Limited Partnership space and can be used for peer comparison, relative valuation, or portfolio diversification. Comparing MAINGATE MLP 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

MAINGATE MLP Market Strength Events

Assessing the market strength of MAINGATE MLP mutual fund provides investors with a clearer picture of how the security reacts to evolving market dynamics. These indicators can be used to identify periods when trading Maingate Mlp Fund is most likely to be profitable.

MAINGATE MLP Risk Indicators

The analysis of MAINGATE MLP's basic risk metrics provides a foundation for forecasting its future price and managing investment risk. Identifying the magnitude of risk in MAINGATE MLP's provides context to choose between accepting or hedging their exposure.
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 MAINGATE MLP

The amount of media and story coverage tied to Maingate Mlp Fund can signal where market attention is concentrating at the moment. This is most useful when investors want to understand why a security is suddenly drawing more public discussion.

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.