Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.

A Temporal Pool Learning Algorithm Based on Location Awareness

Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.

A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn’s Self-Nomination

As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. To overcome these disadvantages, we propose a fast spatial pool learning algorithm of HTM based on minicolumn’s nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding. We have implemented the prototype of the algorithm and carried out experiments on three datasets. It is verified that the training time overhead of the proposed algorithm is almost unaffected by the encoding length, and the spatial pool becomes stable after fewer iterations of training. Moreover, the training of the new input does not affect the already trained results.

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

Cognitive Agent for the Quality Management in Flexographic Printing on Packages

The aim of the chapter is to develop an approach for improving quality management in flexographic printing on packages using cognitive agent. A hybrid agents' architecture based on the learning intelligent distribution agent architecture (LIDA) and hierarchical temporal memory has been developed. Such approach has not been developed before; therefore, it is the main contribution of this chapter. The first part of the chapter presents the introduction to the research problem and background. Next, research methodology and the LIDA cognitive agents have been described. The main part of chapter presents the cognitive agent's architecture and functionality related to quality management in flexographic printing. The last part presents discussion, future works, and major conclusions.