Materials Analysis Stock Forecast - Triple Exponential Smoothing

Triple exponential smoothing for Materials Analysis - 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 Materials Analysis 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 Materials Analysis price movement. However, neither of these exponential smoothing models address any seasonality of Materials Analysis.

Materials Analysis Triple Exponential Smoothing Price Forecast For the 2nd of February

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Materials Analysis Technology on the next trading day is expected to be 246.89 with a mean absolute deviation of 2.81, mean absolute percentage error of 13.40, and the sum of the absolute errors of 165.79.

Materials Analysis Stock Forecast Pattern

Backtest Materials Analysis

Materials Analysis Price Prediction

Buy or Sell Advice

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 Materials Analysis stock data series using in forecasting. Note that when a statistical model is used to represent Materials Analysis stock, 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. As with simple exponential smoothing, in triple exponential smoothing models past Materials Analysis 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 Materials Analysis Technology observations.

Predictive Modules for Materials Analysis

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Materials Analysis. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.

Hype Prediction Low Estimated High 245.52 247.00 248.48 Details

Intrinsic Valuation Low Real High 222.13 223.61 271.70 Details

Bollinger Band Projection (param) Low Middle High 238.20 249.43 260.67 Details

Materials Analysis Related Equities

Risk & Return

Correlation

Materials Analysis Market Strength Events

Materials Analysis Risk Indicators

Pair Trading with Materials Analysis

One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Materials Analysis position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Materials Analysis will appreciate offsetting losses from the drop in the long position's value.

Additional Tools for Materials Stock Analysis