 Agilent Technologies Inc  USA Stock   USD 68.79 0.05 0.0727% 
Investors can use this prediction interface to forecast Agilent Technologies historic prices and determine the direction of Agilent Technologies Inc future trends based on various wellknown forecasting models. However looking at historical price movement exclusively is usually misleading. Macroaxis recommends to always use this module together with analysis of Agilent Technologies 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 crossverify it against solid analysis of Agilent Technologies Inc systematic risks associated with finding meaningful patterns of Agilent Technologies fundamentals over time. Check also
Historical Fundamental Analysis of Agilent Technologies to crossverify your projections.
Investment Horizon

30 Days
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A naive forecasting model for Agilent Technologies is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Agilent Technologies Inc value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.
Given 30 days horizon, the value of Agilent Technologies Inc on the next trading day is expected to be 67.897721
Agilent Technologies Inc Prediction Pattern
Agilent Technologies Forecasted Value
November 17, 201768.79
Market Value
 67.9 Next Trading Day Expected Value  
Model Predictive Factors
AIC  Akaike Information Criteria  35.9757 
Bias  Arithmetic mean of the errors  None 
MAD  Mean absolute deviation  0.4483 
MAPE  Mean absolute percentage error  0.0066 
SAE  Sum of the absolute errors  7.6209 
This model is not at all useful as a mediumlong range forecasting tool of Agilent Technologies Inc. This model really is a simplistic model, and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly. Instead, consider using either the moving average model, or the more general weighted moving average model with a higher (i.e. greater than 1) number of periods, and possibly a different set of weights.