Australian National University

Wenyi Pi

Neo4j APOC Library Use Case

 Author

Wenyi Pi (

 ORCID:

0009–0002–2884–277)

 Introduction

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Introduction to APOC: Enhancing Neo4j Capabilities

Xuzeng He

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called

 nodes

(or vertices) and their relations are called

 edges

(or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Recent Advances in using Machine Learning with Graphs

Qingqin Fang

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Unveiling Research Trends through OpenAlex Visualization

Vaibhav Khobragade

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.

Ethics and AI: Confronting the Challenges Ahead

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Enhancing Language Models: The Role of Knowledge Graph Augmentation in Overcoming LLM Challenges

Enhancing Data Interactivity with LLMs and Neo4j Knowledge Graphs


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771)

 Introduction

Since OpenAI launched ChatGPT, a

 large language model (LLM)

based chatbot, in 2023, it has set off a technological wave.

The integration of large language models (LLMs) with Neo4j-based knowledge graphs

An Introduction to RA-CM3, MuRAG and RACE Author  Xuzeng He (ORCID: 0009-0005-7317-7426)  Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Understanding Sequential Data Modelling with Keras for Time Series Prediction

 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Beginner’s Guide to Recurrent Neural Networks (RNNs) with Keras

Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Virtual assistants have popped up on numerous websites over the years.

Harnessing The Power of Knowledge Graphs in Question Answering

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph

Author Qingqin Fang (ORCID: 0009–0003–5348–4264)

 Introduction

Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Hallucination in Large Language Models and Two Effective Alleviation Pathways

How to efficiently retrieve information for different applications

Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Aishwarya Nambissan

Swinburne University of Technology

Amir Aryani

A brief overview of different types of clustering techniques and their algorithms. Authors Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Amir Aryani (ORCID: 0000-0002-4259-9774)

 Background

Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels.

19 Clustering Techniques

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

The human brain is more extraordinary than any machine we could build. From an early age, many of us gain the ability to comprehend what our eyes tell us and articulate it. Furthermore, we combine evidence from all our senses to reason.

How Much Can Vision Language Models Really “See”?

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

How to use GPT-4V For Stoplight Report

Dhruv Gupta

Improving the performance of Large Language Models Author  Dhruv Gupta (ORCID: 0009-0004-7109-5403)  ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

RAG: The next big thing after LLMs?

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Automated Knowledge Graph Construction with Large Language Models

Nakul Nambiar

Zhuochen Wu

Authors:  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Zhuochen Wu (ORCID: 0009-0000-5642-5348)  Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data.

How to use GPT API to export a research graph from PDF publications

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Recent Advances in Using Machine Learning with Graphs — Part 2

Dhruv Gupta

Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

Automatic Prompt Engineering - Cobus Greyling - Medium

Prompt Engineering — Part 2


 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Prompt Engineering — Part 2

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs


 How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

When searching for information on the website, it is common to come across a flood of

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and…

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Combining Knowledge Graphs With Language Models for Interpretability

Unlocking the power of language models: A deep dive into BERT


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Language Models: Deep Dive into BERT

Large Language Models for Fake News Generation and Detection Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers.

Are Large Language Models Our Allies or Enemies in the Fight Against Fake News?

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Transformers Models in NLP

From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large Language Models (LLMs) have achieved remarkable success.

Three Paradigms of RAG

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) PubMed is a free Web literature search service developed and maintained by the National Center for Biotechnology Information (

 NCBI

), and it is also a part of NCBI’s Entrez retrieval system that provides access to a diverse set of 38 databases.

Enhancing PubMed: from a Medical Database to beyond

Aland Astudillo

How to use GROBID to extract text from PDF  Author  Aland Astudillo (ORCID: 0009-0008-8672-3168)  GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

How to use GROBID

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse.

How to use Large Language Models to tag your data: A complete tutorial

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Deeper understanding of In-Context Retrieval-Augmented Language Models

Amir Aryani

Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Large Language Models and Knowledge Graphs: Ways to combine them

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Prompt Engineering

The AI Helper Turning Mountains of Data into Bite-Sized Instructions

Author Aland Astudillo (ORCID: 0009-0008-8672-3168) LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.

What is LLMLingua?

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A

 knowledge graph

(graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Exploring Methods of Cypher Query Optimisations

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models   Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author  Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction   Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

Authors  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774)  Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.

Mini RAG using Neo4j

Tools and Platform for Integration of Knowledge Graph with RAG pipelines.

Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.

Unveiling the Synergy: Retrieval Augmented Generation (RAG) Meets Knowledge Graphs

Author  Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition   A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Research Collaboration Network

A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors  Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609)  The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.

Typesense and Neo4j in a hybrid information retrieval solution

Improving the performance and application of Large Language Models

Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

Multimodal Large Language Models for Misinformation Detection and Reasoning

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.

RNNs vs GRUs vs LSTMs

A Unified and Collaborative Framework for LLM Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

RAG 2.0 is Coming?

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

NLU vs. NLG: Unveiling the Two Sides of Natural Language Processing

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

An Introduction to Recurrent Neural Networks (RNNs)

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Generative AI’s Leap into Video in 2024 and its Ethical Horizon

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Question Answering enhanced by Knowledge Graph

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Knowledge Graphs: Deep Dive into OpenAlex

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

Automated Knowledge Graph Construction with Large Language Models — Part 2

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Brief Introduction to the History of Large Language Models (LLMs)

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

The longer the context, the better? Unlimited Context Length in Megalodon

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

The Journey of Large Language Models: Evolution, Application, and Limitations

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Understanding the Balance between Internal Knowledge and External Sources

Author Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

When RAG and LLM Conflict: Who Will AI Listen to?

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Navigating the Long Context Conundrum: Challenges in Language Models’ Information Processing

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution.

Fine-tuning Large Language Models: A Brief Introduction

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Prompt engineering: A Way to Smartly Use AI

Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.

Large Language Models for Code Writing: Security Assessment

Stories by Research Graph on Medium

#### Understanding Sequential Data Modelling with Keras for Time Series Prediction

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*Ob-EpFxiacU7rIQ2" />
<figcaption>Image created in <a
href="https://www.canva.com/">https://www.canva.com/</a></figcaption>
</figure>

### **Author**

- Wenyi Pi (**ORCID**:
 [0009--0002--2884--2771](https://orcid.org/0009-0002-2884-2771))

### Introduction

Recurrent Neural Networks (RNNs) are a special type of neural networks
that are suitable for learning representations of sequential data like
text in Natural Language Processing (NLP). We will walk through a
complete example of using RNNs for time series prediction, covering data
preprocessing, model building, training, evaluation, and visualisation.
This article will introduce Keras for RNN and provide an end-to-end
system using RNN for time series prediction.

#### **Introduction to Keras**

Unlike traditional neural networks which assume that all inputs and
outputs are independent of each other, RNNs make use of sequential
information with the output dependent on the previous computations. For
a more detailed introduction of RNN, please find this article [RNN
Introduction](https://medium.com/@researchgraph/an-introduction-to-recurrent-neural-networks-rnns-802fcfee3098).
Keras, developed by Google, is a high-level deep learning API known for
its modular, user-friendly, and extensible features, facilitating the
implementation of neural networks. In contrast, TensorFlow is an
end-to-end open-source deep learning framework that provides
comprehensive support for various machine learning tasks. Acknowledging
the benefits of both platforms, Google merged Keras into TensorFlow as
its official high-level API, enhancing accessibility and enabling
support for multiple backend neural network computations. This
integration streamlines the implementation of neural networks, providing
users with simplified model development processes.

Below is a basic pipeline when using Keras for building a model:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*HH0q0-y3JqMeNHB3" />
<figcaption>Building a model in Keras. Image from: <a
href="https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-is-keras#what_is_keras">What
is Keras</a></figcaption>
</figure>

1. **Define a network**: Define different layers in the model and the
 connections between them. Keras has two main model types: Sequential
 and Functional.
2. **Compile a network**: Convert code in a machine understandable
 format. To compile code, we define the **loss function** to
 calculate losses in the model, **optimiser** to reduce the loss, and
 **metrics** to find the model accuracy.
3. **Fit the network**: After compiling the model, train and fit model
 on the data.
4. **Evaluate the network**: After fitting the model, evaluate the
 error in the model.
5. **Make predictions**: Make predictions on new data.

### A complete application for time series prediction

#### **Set up the environment**

We assume that you have Python installed (version 3.11.3). We need to
have the TensorFlow and the TensorFlow Datasets packages installed on
the system. In this tutorial we used the TensorFlow version 2.13.0.

Use:

 pip install tensorflow
 pip install matplotlib

#### **Import the necessary packages**

 from pandas import read_csv
 import numpy as np
 import math
 from keras.models import Sequential
 from keras.layers import Dense, SimpleRNN
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.metrics import mean_squared_error
 import matplotlib.pyplot as plt

#### **Read data from url**

 #Read data from given url and extract the second column
 def read_data(url):
 df = read_csv(url, usecols=[1], engine='python')
 data = np.array(df.values.astype('float32'))
 #Normalise data into (0,1) range 
 scaler = MinMaxScaler(feature_range=(0, 1))
 data = scaler.fit_transform(data).flatten()
 n = len(data)
 return data, n

 sunspots_url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/monthly-sunspots.csv'
 data, n = read_data(sunspots_url)

#### **Split data into training and test sets**

 #Splitting data into train and test based on split ratio
 def get_train_test(split_percent, data):
 n = len(data)
 split = int(n * split_percent)
 train_data = data[:split]
 test_data = data[split:]
 return train_data, test_data

 split_percent = 0.8
 train_data, test_data = get_train_test(split_percent, data)

The
[MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
scales and translates each feature individually so that it is in the
given range such as \[0, 1\]. The data are split into 80% training set
and 20% testing set.

#### **Reshaping data for Keras**

This step is to prepare data for Keras model training. It is important
to reshape input data in preparing data for use with a neural network.
This helps to ensure that the network can make use of the data. For
sequence prediction tasks like time series forecasting, the input shape
typically is (**total_samples** x **time_steps** x **features**).
**total_samples** is the number of data points in the dataset.
**time_steps** refers to the number of historical observations used to
predict the next time step. **features** denote the number of variables
used for prediction.

 #Reshape data into input-output pairs with specified time steps
 def get_XY(dat, time_steps):
 Y_ind = np.arange(time_steps, len(dat), time_steps)
 Y = dat[Y_ind]
 rows_x = len(Y)
 #Prepare Training and testing data
 X = dat[range(time_steps*rows_x)]
 X = np.reshape(X, (rows_x, time_steps, 1)) 
 return X, Y

 time_steps = 12
 trainX, trainY = get_XY(train_data, time_steps)
 testX, testY = get_XY(test_data, time_steps)

#### **Create the RNN Model and Train**

 #Define the RNN model
 def create_RNN(units, dense_units, input_shape, activation):
 model = Sequential()
 model.add(SimpleRNN(units, input_shape=input_shape, 
 activation=activation[0], return_sequences=True))
 model.add(Dense(dense_units, activation=activation[1]))
 #Compile the model
 model.compile(loss='mean_squared_error', optimizer='adam')
 return model

 model = create_RNN(units=3, dense_units=1, input_shape=(time_steps,1), 
 activation=['tanh', 'tanh'])
 model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)

The function above returns a sequential model with two layers, a
**SimpleRNN** layer with three units and a **Dense** layer with one
unit. It defines the input shape at 12×1 and *tanh activation* function
is used. The
[SimpleRNN](https://sztensorflow.org/api_docs/python/tf/keras/layers/SimpleRNN)
layer utilises the TensorFlow Keras API's implementation, ensuring that
the hidden state is recurrently fed back as input to the next state.The
[Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)
layer is a standard fully connected layer which performs a linear
transformation of the input data followed by an activation function.

A **loss function** measures the difference between the predicted output
of a model and the actual output, while an **optimizer** adjusts the
model's parameters to minimise the loss function. The model is compiled
with the *mean squared error loss* and *Adam gradient descent
optimiser*.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*3Iiyl1nN47inPdCl" />
<figcaption>Model training log output.</figcaption>
</figure>

The model performed as expected, with losses continuing to decline in
each period. This reduction in losses means that the model is
effectively learning from the training data and making progress in
optimising its parameters to better fit the training data. This is a
positive sign that the model is moving towards a solution and improving
its predictive capabilities over time.

#### **Compute and print the Root Mean Square Error (RMSE)**

 #Get error of predictions to evaluate it
 def print_error(trainY, testY, train_predict, test_predict): 
 train_predict = train_predict.reshape(-1)
 test_predict = test_predict.reshape(-1)
 train_rmse = math.sqrt(mean_squared_error(trainY,train_predict))
 test_rmse = math.sqrt(mean_squared_error(testY, test_predict))
 print('Train RMSE: %.3f RMSE' % (train_rmse))
 print('Test RMSE: %.3f RMSE' % (test_rmse)) 

 #Make predictions
 train_predict = model.predict(trainX)
 test_predict = model.predict(testX)
 #Mean square error
 print_error(trainY, testY, train_predict, test_predict)

<figure>
<img src="https://cdn-images-1.medium.com/max/896/0*XIZtghZl8moWnsiz" />
<figcaption>Root Mean Squared Error.</figcaption>
</figure>

RMSE is a measure of the differences between values predicted by the
model and the actual observed values. Lower RMSE values typically
indicate better performance of the model. According to a commonly used
rule of thumb, RMSE values falling between 0.2 and 0.5 indicate that the
model can relatively predict the data accurately. We can find from the
result above, the test RMSE is 0.087 RMSE. Given this value falls lower
than 0.2, it can be inferred that the RMSE values are relatively low.
Consequently, it suggests that the model is performing well on the
testing datasets.

#### **Visualise the Results**

 #Plot the result
 def plot_result(trainY, testY, train_predict, test_predict):
 actual = np.append(trainY, testY)
 predictions = np.append(train_predict, test_predict)
 rows = len(actual)
 plt.figure(figsize=(15, 6), dpi=80)
 plt.plot(range(rows), actual)
 plt.plot(range(rows), predictions)
 plt.axvline(x=len(trainY), color='r')
 plt.legend(['Actual', 'Predictions'])
 plt.xlabel('Observation number after given time steps')
 plt.ylabel('Sunspots scaled')
 plt.title('Actual and Predicted Values. The Red Line Separates The Training And Test Examples')

 plot_result(trainY, testY, train_predict, test_predict)

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*5-0aATxZV4Sk1bFw" />
<figcaption>Visualising the original data and resulting
predictions.</figcaption>
</figure>

According to the graph, we can see that the predicted values closely
match the actual values even in the test set portion, which indicates
that the RNN model has learned the underlying patterns in the data well
and it can effectively make predictions.

### **Conclusion**

In this article, we have shown how to implement a simple Recurrent
Neural Network model for time series prediction using Keras with the
TensorFlow Python package. By following the step-by-step process,
beginners can gain a foundational understanding of RNNs and their
application in sequential data modelling.

### **References**

- Saeed, M. (2023) Understanding simple recurrent neural networks in
 Keras, MachineLearningMastery.com. Available at:
 <https://machinelearningmastery.com/understanding-simple-recurrent-neural-networks-in-keras>.
- Working with RNNS: Tensorflow Core (2023) TensorFlow. Available at:
 <https://www.tensorflow.org/guide/keras/working_with_rnns>.
- Zhou, V. (2020) *Keras for beginners: Implementing a recurrent neural
 network*, *Victor Zhou*. Available at:
 <https://victorzhou.com/blog/keras-rnn-tutorial>.

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