|F -- USA Stock|| |
USD 9.59 0.26 2.64%
Investors can use this prediction interface to forecast Ford Motor historic prices and determine the direction of Ford Motor Company 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 Ford Motor 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 Ford Motor Company systematic risks associated with finding meaningful patterns of Ford Motor fundamentals over time. Additionally see Historical Fundamental Analysis of Ford Motor
to cross-verify your projections.
Double exponential smoothing - also known as Holt exponential smoothing is a refinement of the popular simple exponential smoothing model with an additional trending component. Double exponential smoothing model for Ford Motor works best with periods where there are trends or seasonality.
Given 30 days horizon, the value of Ford Motor Company on the next trading day is expected to be 9.695537
Ford Motor Prediction Pattern
Ford Motor Forecasted Value
September 25, 2018
Model Predictive Factors
|AIC||Akaike Information Criteria||Huge|
|Bias||Arithmetic mean of the errors ||0.0123|
|MAD||Mean absolute deviation||0.1057|
|MAPE||Mean absolute percentage error||0.011|
|SAE||Sum of the absolute errors||1.5848|
When Ford Motor Company 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 Ford Motor Company trend in the prices. So in double exponential smoothing past observations are given exponentially smaller weights as the observations get older. In other words, recent Ford Motor observations are given relatively more weight in forecasting than the older observations.