Seeing Machines Stock Forecast - Polynomial Regression
| SEE Stock | 3.55 0.15 4.41% |
The Polynomial Regression forecasted value of Seeing Machines Limited on the next trading day is expected to be 3.76 with a mean absolute deviation of 0.16 and the sum of the absolute errors of 9.78. Seeing Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Seeing Machines stock prices and determine the direction of Seeing Machines Limited's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Seeing Machines' historical fundamentals, such as revenue growth or operating cash flow patterns.
Seeing |
Seeing Machines Polynomial Regression Price Forecast For the 13th of December 2024
Given 90 days horizon, the Polynomial Regression forecasted value of Seeing Machines Limited on the next trading day is expected to be 3.76 with a mean absolute deviation of 0.16, mean absolute percentage error of 0.04, and the sum of the absolute errors of 9.78.Please note that although there have been many attempts to predict Seeing Stock prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Seeing Machines' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Seeing Machines Stock Forecast Pattern
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Seeing Machines Forecasted Value
In the context of forecasting Seeing Machines' Stock value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Seeing Machines' downside and upside margins for the forecasting period are 0.26 and 7.26, respectively. We have considered Seeing Machines' daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Seeing Machines stock data series using in forecasting. Note that when a statistical model is used to represent Seeing Machines 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.| AIC | Akaike Information Criteria | 114.9736 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.1604 |
| MAPE | Mean absolute percentage error | 0.0403 |
| SAE | Sum of the absolute errors | 9.7845 |
Predictive Modules for Seeing Machines
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Seeing Machines. 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.Other Forecasting Options for Seeing Machines
For every potential investor in Seeing, whether a beginner or expert, Seeing Machines' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Seeing Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Seeing. Basic forecasting techniques help filter out the noise by identifying Seeing Machines' price trends.Seeing Machines Related Equities
One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Seeing Machines stock to make a market-neutral strategy. Peer analysis of Seeing Machines could also be used in its relative valuation, which is a method of valuing Seeing Machines by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Seeing Machines 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 Seeing Machines' 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 Seeing Machines' current price.| Cycle Indicators | ||
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| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
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| Volume Indicators |
Seeing Machines Market Strength Events
Market strength indicators help investors to evaluate how Seeing Machines stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Seeing Machines shares will generate the highest return on investment. By undertsting and applying Seeing Machines stock market strength indicators, traders can identify Seeing Machines Limited entry and exit signals to maximize returns.
Seeing Machines Risk Indicators
The analysis of Seeing Machines' basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Seeing Machines' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting seeing stock prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 2.42 | |||
| Standard Deviation | 3.49 | |||
| Variance | 12.19 |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.
Also Currently Popular
Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.Other Information on Investing in Seeing Stock
Seeing Machines financial ratios help investors to determine whether Seeing Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Seeing with respect to the benefits of owning Seeing Machines security.