General Mills Polynomial Regression

GIS -- USA Stock  

Fiscal Quarter End: November 30, 2019  

Investors can use this prediction interface to forecast General Mills historic prices and determine the direction of General Mills future trends based on various well-known forecasting models. However looking at historical price movement exclusively is usually misleading. Macroaxis recommends to always use this module together with analysis of General Mills historical fundamentals such as revenue growth or operating cash flow patterns. Although naive historical forecasting may sometimes provide an important future outlook for the firm we recommend to always cross-verify it against solid analysis of General Mills systematic risks associated with finding meaningful patterns of General Mills fundamentals over time. Please also check Historical Fundamental Analysis of General Mills to cross-verify your projections.
Horizon     30 Days    Login   to change
General Mills polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for General Mills as well as the accuracy indicators are determined from the period prices.
Given 30 days horizon, the value of General Mills on the next trading day is expected to be 54.225939

General Mills Prediction Pattern

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General Mills Forecasted Value

October 14, 2019
Market Value
Expected Value

Model Predictive Factors

AICAkaike Information Criteria117.1393
BiasArithmetic mean of the errors None
MADMean absolute deviation0.4529
MAPEMean absolute percentage error0.0084
SAESum of the absolute errors27.6287
A single variable polynomial regression model attempts to put a curve through the General Mills historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm

Volatility Measures

General Mills Risk Indicators