DATA MODUL Stock Forecast - Triple Exponential Smoothing

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

DATA MODUL Triple Exponential Smoothing Price Forecast For the 7th of January

Given 90 days horizon, the Triple Exponential Smoothing forecasted value of DATA MODUL on the next trading day is expected to be 27.17 with a mean absolute deviation of 0.59, mean absolute percentage error of 0.63, and the sum of the absolute errors of 34.90.

DATA MODUL Stock Forecast Pattern

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DATA MODUL Price Prediction

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DATA MODUL Forecasted Value

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 DATA MODUL stock data series using in forecasting. Note that when a statistical model is used to represent DATA MODUL 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 DATA MODUL 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 DATA MODUL observations.

Predictive Modules for DATA MODUL

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as DATA MODUL. 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 23.97 26.60 29.23 Details

Intrinsic Valuation Low Real High 24.32 26.95 29.58 Details

Other Forecasting Options for DATA MODUL

For every potential investor in DATA, whether a beginner or expert, DATA MODUL's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. DATA Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in DATA. Basic forecasting techniques help filter out the noise by identifying DATA MODUL's price trends.

DATA MODUL Related Equities

Risk & Return

Correlation

DATA MODUL Technical and Predictive Analytics

The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of DATA MODUL's price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of DATA MODUL's current price.

DATA MODUL Market Strength Events

DATA MODUL Risk Indicators

Additional Tools for DATA Stock Analysis