The Study Aims to Forecast the Opening Prices of Prominent Stocks Across Four Distance Industries: Bank of Communications (JT), Royalflush (THS), Hengrui Pharmace uticals (HR), and GREE ELECTRIC Appliances (GL). These Industries Span Finance, Information Technology,Pharmaceutical, and ManuoFacturing, Command Significant Market Influence and Attention. ED for its comerhensive, account, and timely Financial data and information service. For the preliminary infestification, Daily Stock Data Was Selected ford forEach Entity From January 2, 2018, to September 14, 2023, Encompassing a Total of 5,534 data points. Commly user Cludes Both Price Data (E.G., Opening Price, Closing Price) and Trading Data (E.G., P/E Ratio, P/S Ratio). Price Data Directly Mirrs Stock Price Trends, While Trading Data Reflects Stocks Throughs and Profitability, C ONSIDERED HIGH-QUALITY DATA in Stock ForeCasting, Capturing Diverse Information IMPACTING Stock Price Trends. Each. Each. Each. Each. Each. Each. Each.Stock’s DataSet Involves Seven Primary Price Factors: Opening Price (Open), High Price (HIGH), Low Price (Low), CLOSING Price (CLOSE), Price-Earnings O (PE), Price-TO-BOOK RATIO (PB),And Price-TO-SALES RATIO (PS)Agra Investment. Here, The Opening Price Serves as the target variable for preditION, while the remaining six attribute as character . The high, low, and Close Signify Stock Price Fluctifications, Repreenting Maximum Demand,Minimum SUPPLY, and Overall Market Valuation of the Stock, Respectively. IDE RISK, And Yield, TheReby IMPACTING The Stock’s Opening Price. On the Other Hand, PE, PB, and PS Reflect AStock’s value level, Signifying Market Expectations Concerning A Company’s Profitability, Net Worth And Sales Capacity. ThesEse Expectations Shape Market Percepts of a Company’s Future Prospects, Consequently Influenecing The Stock Price. Hele E high, Low, CLOSE, PE, PB, and PS Attributes are chosen as characteristic variables to predict the stock’s Opening Price.
The integration of attention mechanisms into models for stock prediction is crucial due to the complex interplay of multiple factors influencing stock trendsSimla Investment. Attention mechanisms have gained prominence in various neural network architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), And Transformers. The Mechanisms Are Particularly Adept at Processing Financial Data, Time Series Data, and Natural LANGUAGE DATA. However ES When Currentation Mechanisms Are Apply to Excessively Long Sequences. In Such Scenarios, These Mechanis MesMS OFTEN Struggle to FullyComprehend The Relationships and DependenCies Between Sequence Elements, Leading to ISSUES LIKE Overfitting and Poor Generalization. To address the Limitations, PREV IOUS Work Like Se38 Has Focused on Modeling The Significance of Each Feature Channel. by Enhancing the Attention and Expressiveness of Different Channel Features, SeNot Only Improves Network Performance But Also EFFECTIVELY Redundant Information BetWeen Channels, thereby enhancing network stability. Buildingon , Cbam39 InCorporated Spatial Attention Into Se, Thereby Enriching the Repressation of Features Both Spatially and Channel-Wise. Xtracting spatialPatterns and Variations in Time Series Data. Despite the advancements, Solely Relying On Channel Attention (Like SE) or Spatial Attentation often Falls Short In E predition tasks such as stock volume prediction. The reason is the lack of localization or comprehensive feature information. CBAMAttemplted to Address this by synthesizing information, but it still lacks in capturn interactions and dependenencies elements. GS, We Intropique the Probabilistic Positional Attention (PPA). endnciesWithin sequence elements. The structure of ppa, As depict in Fig. 3-PPA, Represses a Significant Advancement in Attention Mechanisms, Offering a More Sophisticated ApProach to Handling the Complexities T in Stock Volume Predict Tasks. This Approach Ensures Not only the Extrabian FEATURES But Also the Preserviation of their ContextualAnd posityal s.
WE FIDD POSITIONAL Encodings to the Input Data to Enable the Model to Understand the Order of Elements in The Sequence. the input data, help the model distinguish between elements at the different positions in the sequence.Are Calculated as in EQS. (1) and (2).
where pos is the position, from 0 to the maximum like of the sequency. I is the index of the directgency in the position is encoded, Typically From 0 to m- s the hidden state dimension of the model). PE(POS, 2i) Is the Position Encoded Value for Position Pos and Dimension 2i. PE (POS, 2i+1) is the positive-coded value for posity 2i+1.+1.+1.
Next, We Integraate A Multi-Head Attention Mechanism INTO The PPA MODULE. In this Mechanism, Attention is divided into multiple ‘heads’, Each Off PENDEENTLY PROCESSESSSES DIFFERENT REPRESENTAL SUBSPACES of the Input Data, and then Combines the Results of the PROCESSES.This allows the model to capture features of the sequence from multiple perspectives, improving the model’s underness of the input data.
In Addition, We iISS-O cantention meghanism, which is partially imprtant when dealing with sequence-sequence tasks. Pays Attention Not only to its own input sequence but also to the information of another sequence. forExample, in Machine Translation, The Attention Layer of the Decoder Pays Attention to the Sequence Output by the Encoder to Capture The CorresPondency E and target languages.
Next, We Combine A New Mechanism for Computing Sampled Query Key Interactions with The _PROB_QK FUNCTION. Lexity and Increase EFFICIENCY WHILE CAILE CAPTING Important Information. The Output of the Attention MeChanism by A Factor and Optionally l Encoding, And the Mask_flag Parameter Determines WHETHER A Mask Should be appliced during the moment. Scarded. The output can also include defaled attents if the output_address flag is set.
SPECIFICALLY, The Relevant Information in the Input Sequence is Captudured by Computing the Sampled Query Cross Terms (Q_K Sampling) USING A Combining of Sampled Query Key Interactions, Scaling, Positional Coding, and Optional Masking. To Reduce Computational Complexity and Increase Efficience, Q_KSampling is computed as in eq. (3).
After Computing the Sparsity Measure M, The Top N_TOP Queries with the Highest Sparsity Measure are selected. Votal for Subsequent Computation of Attention-weight contextual information. The context initialization process varies based on the mask valag. WHEN MAS k_flagIs enabled, the context is initialized use the cumulative sum; Otherwise, the Avera fit the value is computed to initialize the context. Easure m is called
The Integration of Positive Encoding with Multi-OsTention, Cross-ATATENTION CAPABILITIES, and Computational Sampling of Query Key Interactions Enhances The Model’s Proficience in Processing Sequence Data. This Multificted Approach Not only Deepens the Modestanding of the Relationships AN d DependenCies between sequence elements butAlso Empowers It Efficiently Discern and Leverage Intricate Patterns within Sequency. Such Capability is EspeCially Crucial in Tasks that Involution. Lyzing Complex Time Series, Like Stock Market ForeCasting, where comprehending and prediting temporant dynamics is essential.
In Subsection "Effectiveness of PPA" of PPA "of PPA" of PPAILED Analysis of the PPA MODULE’S IMPACT. ERFORMANCE Enhancement Over The Baseline Model. This Empirical Evidence Not only value the theRERETIRTICAL SOUNESSSSSS of the Proposed Improvements But AllsoSoConfirms Their Superior Performance in Practical Applications. The Results Underscore The Effical of the PPA MODULE In Engel’s Analytical Cap. Abilities, theReby Making it a valuable tool for complex sequence analysis tasks, particularly in the realm of finishing.
The input of too long sequences in Stock ForeCasting Leads to An Increase in Computational Resources and A DeCrease in Model GeneralY.The Convolution o Perating of the encoder in the incidence model can effectively Capture Local Patterns and Trends in Stock Time Series Data, Which Can IMPROVEThe Model’s Ability to Perceive Local Features.howver, A Single One-Dimensional Convolutional FEACTION is Limited Effectively Handle TOO G input sequences, and it may Result in the Dimensionality of the Output Features Being too high, which increases computational. TheReface, To Avoid The PROBLEM of Reduced Model Generalivity When the Input Sequence LONG, this Paper Proposes A Multi-Scale Timing Feature Convolution (MTF C), The Structure of Which is Shown in Fig. 3-MTFC.
While The Common 3 × 3 Convolution has ben baby to catch time. TED, Which Does Not Capture All the Information in Large DataSets and AFFECTS The Accuration of the Predictions. In.Addition, A FIXE Convolutional Filter Considers All Sampling Points in the Time Series Equity, Which Can Limit the Ability of the Model to Express Feates of the Time Series. To Address this isSue, we use MTFC Instead of One-Dimensional ConvolutionThe Convolutional Layer. First, We Draw on the Core Idea of Coordined Attention (CA) 40, Due to the Specification of the Input Time Series Data, We Decompose It Into WO DIMENSIONS, Time Step and Feature, for Feature Extraction Sepately by Two ParallelllPooling layers, and Through This Operation, The Model Can Focus on Which Changes Are More Significant at Differents in Time, Which Is PARTILARLETANT For NDERSTANDING The Dynamic Characteristics of Stock PriceS Over Time.
Owing to the Dynamic Characteristics of Stock Market Data Across Multiple Time Scales, Important Information May Be Concept Within Different Time s analysis. To Address this, we have designed a multi-scale confidence consisting of three branches. Convolution Kernel Size of1 × 1, 3 × 3 and 5 × 5 is used for computation in this structure. Subsequently, Three Normalization Layers Stabilize the Network, Preventing Disperse. Are then ConcateNated.
Regarding the feature diarsion, tractitional convification process all features simultaneously, increasing the network’s computational load and potentially UCing Generalization Ability. To Mitigate this, we use a gative mechanis, to categorize features as IMPORTANTRTANT. Important Features Undergo A More Ive 3 × 3 ConvolutionTo FULLY CAPTURE T. In Contrast, Unimportant Features Are PROCESSED with a More Cost-EFFECTIVE 1 × 1 Convolution to CONSERVEAL ES. After Convolution Normalization and Sigmoid Activation, We Obtain the Final Output Feature Sequence.
SPECIFICALLY, We First Use AvgPool to Process The Input Vector Sequence and Decompose It into Two Dimensions, Time Step and Feature. Step Feature Sequences of the Two Dimensions Into Two Specially Designed Convolutions and Use Multi-Scale ConvolutionAnd Gated Convolution Operation to expectable features from the two dimensions. Finally, we Output the expetracted time-server sequency, Which Are Splicd and PKolkata Stocks. ROCESED Into the Time-Series Prediction Network to Perform accountMultiscale Convolution and Gated Convolution Operations, Which are used to polform the Next Prediction Task. Experiments on MTFC are described TIVESS of MTFC ".
In Stock Prediction Tasks, Mutation Points in the Data Significantly Impactly. is pivotal. An Adaptive Learning Rate Adjustment Strategy Can Help the Model CONVERGE to the Optimal Solution More Swiftly, CRUCIAL ForACHIEVING Accurate Segmentation of Edge-Blurred ImagesNew Delhi Investment. The Earlier Stochastic Gradient Descent (SGD) 41 Optimizer, Which Updates Parameters by Randomly G SAMPLES and Optionally Incorporates A Momentum Parameter to Expedite the Learning Process, Proves Effective Involving High Curvature, Small But Cons IsterGradients, or Noisy Gradients. Subsequently Proposed was adam’s options options 42, which is more Advanced. It Commences by Computing The Exponential Moving. of the gradient, the information of the prior gradient information. Following this, It Calculaters the Exponent Average of the Squared Gradient to normalize theGradient. The Optimizer Will The Bias in the Estimation of the First and Second Order Moments of the Slope. It updates the parameters Based On HE Ratio of the Square Root of the Correct FIRST Order Moment Estimate to the Square Root ofThe Second Order Moment Estimate. This Intricate Process Alows for Fine-Tuning the Learning Rate of Each Parameter, Enabling a More Rapid Convergence UTION.
At Sudden Change Points in Stock Data, Time Series Prediction Models often Struggle to Priors These Data Points Correctly, Leading to Reduced Robustness and T predictions. DURING The Instance, Optimizers with Adaptive Learning Rates, Such as adam, can face discGradient, Which in Turn Affects The Learning and Generalization Process of the Model. To Tackle This Challenge, We Internet A Note Optimization Algorithm: Ant Article Swarm Optimization (APSO). APSO Enhances The Identification of Mutation Points within the Entire Market Data by Synergizing the.Ant Colony Algorithm (ACO) and PARTICLE SWARM Algorithm (PSO). This Combination Facilities The Explration of A Broader Potential SOLUTION Space, Thus Addressing Th e limitations posed by sunden changes in stock data.
The Optimization Parameter Process in Apso Involves Several Key Steps: Initially, a Colony of ANTS is Established, With Each Ant Symbolizing A Pth EQUENTLY, a Solution). The Positives of the ANTS and their Respective Chosen Paths Are Initialized. Then, DuringEach Iteration of Training, The Velocity of the ANTS (Repressenting The DiverGence Between Their Current Paths and the Optimal Paths) is updated. He Ants are Denoted as P_current. FURTHERMORE, The TERM P (Global Best) is using to replyThe globally optimal path. The formula for update p (global best) is described in the subsequent section.
Where Δ (x, y) is an indicator function that takes the value 1 if x ≠ y and 0 Otherwise, this formula calls the number of points where the two Paths do not Incide at all posteds.
The Path Update is Performed Based on the Current Path and A Set Probability (Assumed to Be 0.5), and for Each Point P_Current [I] On the Set Path Ability Determines WHether It is updated to the corresponding point p_ (Global Best) [i] On the global best path, which can be expressed as
The process of exploing Paths and Updating the Global Optims is Carried Out by Reperation This for the Number of Iterator Between The Global Optimal Solution and the Current Solution, in this way the algorithm isap not onlyTo Maintain the Search for the Current Optimal Solution, but also to explore new solutions and to explore more efficiently in the space of Multimutant Points, for EX Periments on APSO SUBSECTION "Effectiveness of Apso".
Stock Prediction Predicts A Multifaceted Challenge, Influenced By A Myriad of Factors Encompassing Economic, Market, and Political Realms. Ainty and Volatility in this domain needed the precise adjustment of network Parameters in deep Learning Models. TPUTAS CLOSELY As Posses with Real Values. Presently, The Majority of Deep Learning Models Adopt the Mean Square Error (MSE) AS Their Loss Function. However, The MSE Loss Function Exhibitions a Drawback: Its PARTIAL DERIVATIVE IS DIRECTLY PROPORTIONAL to the Error Magnitude.Consequently, as the error diminishes, so do does the partial derivative, Resulting in a Slowdown of the Model’s CONVERGENCENCENCED WHEN Nery Slution. To Overcom This Limity, An Improved Loss Function, The Smoothl1 Loss Function, Emerges as a SolutionNagpur Stock. It amalgamatesThe Stringths of Both the L1 and L2 Loss Functions, Offering Smoolhness and Differentiability Through a SegMentem Strus. Lly Tackle the Challene of Mutation Points Prevalent In Stock Data, this Paper Proposes A Robust Regression Loss Function. LAspects of mse and smoothl1, defining a comprehensive formulation as presented in eq. (7):
This page Introducess a Novel SegMenTED Loss Function Designed to Optimize Inventory ForeCasting Models. Differential ApProach to Varying Error Intervals. When the discrepancy between the model’s predict value and action value is minimal, The Loss Function’s GradientDiminishes as the Error Decreases. This Characteristic Aids in Maintaining High Acacuracy and Prevents Over-Tuning WHEN The Model Nears The OPTIMAL SOLUTION. Conversel y, for larger erotrors, the gradient of this loss function is capped at uper limit of 0.75. This cap serverTo Shield The Model From Excessive Disruptions Caused by Outliers or Extreme Values, thereby enhancing its Robustness. OSS, The SegMented Loss Function Proposed in this Paper Retains A Certain Gradient Level with Dwindling to Zero WhenLing Large Error.This Attribute is partially beneficial for expedition the model’s convergence during training. It Provides a More Effective and Stable Training F or Analyzing Complex Time-Series Data, SUCH As Stock Market ForeCasting. The SegMenTd Loss Function theReBy Represervs A SIGNIFICANT Advancement in the Field, Offerin GAn Improved Approach for Managing the intricacies of time-server analysis.
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