Stock Data and News Analysis: ML Experience

• Time-series forecasting model: As a data scientist, our goal is to provide a system that can help interpret news and the stock data to see how and what factors are impacting the industry. 
• The data extracted will be fed to the neural networks, which can analyze the patterns and habits from the news archive. By usage of extensive training techniques, Neural networks will be knowledgeable to understand news and current data and should be able to predict its worth to buy.

• How much does the data scientist understand Assignment 1 (domain) and Assignment 2 (data)?

• BERT  summarization models: 

  In the given data,  a couple of attributes available in the given equality dataset. For summarizing the data, it is good enough to process only text data by avoiding special symbols irrespective of the data  / length it is having.  After pre-processing and summarizing and analyzing the summarized data, we need evaluated the performance of the text generated by the BERT model against the summary generated by the human which represents recall.  

• Transformer based Sentiment analysis:

  It is important to understand the moving data and their changes with respect to the sentiments provided in the news article. We then employ a model that’s capable of extracting knowledge from the provided article and then establish the kind of sentiment associated with the article. 

  Time-series forecasting model:

  While preprocessing the data, we have come across almost all the fields of the data, what it means and how to analyze it. Keeping this idea in scope we have moved forward by building a simple time-series model to predict the data changes. Once, we have the time-series model built, the sentiment value’s affect on the stock price value changes are detected.

• What models and analysis did the data scientist and AI apply to fulfill the need of the people or the community?

• BERT  summarization models: 

  There are two types of summarization: abstractive and extractive summarization. Abstractive summarization basically means rewriting key points while extractive summarization generates summary by copying directly the most important spans / sentences from a document. For instance, if we are doing some research and need to get a quick summary on a financial dataset having  thousands of records of what we were reading, extractive summarization would be more helpful for the task. 

  Our objective is to build BERT extractive summarization from the content column  presented in the equity dataset. In addition to that, summarization of the  text data   providing input for sentiment analysis helps to analyze the data by minimizing the system resources in terms of time and cost without losing the essence of the original content.

• Transformer based Sentiment analysis:

  The goal of this model is to provide sentiments from the news article and in doing so this model helps to evaluate the fluctuations of the stock prices in relation with the sentiments of the news. This helps the community to answer the question of whether we should really look into news articles provided in the web as a reliable resource for generating insights of the stock price. 

• Time-series forecasting model:

  Time-series forecasting models are ones that are capable to predict future values based on previously captured values. Time-series forecasting is widely used for non-stationary data. Non-stationary data are the data whose metrics vary over time.

  These non-stationary input data (used as input to these models) are usually called time-series. Some examples of time-series include the temperature values over time, stock price over time, price of a house over time etc. So, the input is a signal (time-series) that is defined by observations taken sequentially in time as shown below. There are two types of time-series models used and the results are compared.

1. The ARIMA (Autoregressive Integrated Moving Average) model is a widely used forecasting technique for time-series prediction. This is a machine learning algorithm

2. The LSTM ( Long-short Term Memory) model is another deep learning model used for time-series prediction.

• Can the data scientist estimate and select data for their goals from Assignment 1? Can they map data sets from Assignment 2 onto appropriate ML models?
• Transformer based Classification and summarization models: In order to perform data summarization from equity dataset we need to preprocess the data and perform  summarization using BERT extractive summarizer. This summarization data provides input for the sentiment analysis to generate scores for each record. Original content in terms of length and text: Total No.of. words in an original text = 90836. Summarized content in terms of length and text:Total No.of. words after summarization from original text = 4304
• Time-series forecasting model: While modeling the data, we have extracted the stock market dataset from here and the preprocessing of the data has been done. After the preprocessing of data which includes, collecting the stocks and neglecting the ETFs, analyzing the companies based on the ticker values, etc. we have modeled the data in order to predict the value of the stocks.

• ARIMA model: The preprocessed dataset could be mapped with the model and the values of a particular stock (IBM in our case) varying over time is shown below:

After the modeling is done, the actual and the predicted values are also visualized:

 

• LSTM model: The actual and predicted values are compared and visualized.

• Can the data scientist connect Story 1 with ML models/stories about what a ML model can do? To perform good ML research, what in-depth knowledge and experience with ML algorithms and ML stories does a data scientist need? 

• BERT  summarization models: Before performing summarization and sentiment analysis on the text data for generating scores for each company, we should perform data pre-processing on each record and filter if there are any unnecessary columns which ensure it does not lose the core of the original content before proceeding any type of operations.
• Time-series forecasting model: When modeling the data using the time-series model, since it is a model used for forecasting or future prediction, and also for the non-stationary data, it is suitable for the stock price prediction and when used along with the sentiment analyzed data, it can achieve our goal.
• In order to perform some good research in this area, one has to have knowledge on what is non-stationary data? What are the models used to predict them well? Will the deep neural network play an important role in modeling the data? If yes, which kind of deep learning algorithms can be used? How to integrate the sentiment analysis into the Time-series model and observe necessary changes? These are some of the experiences/expertise one should have in order to model the data and to achieve the goal.
  

• When has to do with the iterations (Calibration 2). How much time did it take for experimentation? How efficient is the modeling/algorithm? 

• Can the data scientist determine the acceptance level of the model (validation with accuracy and runtime performance) considering the targeted users? 

• Where has to do with the learning environment. Where did this experiential learning process take place? For example, it was part of an online Deep Learning course. 

BERT summarization is recent revolutions in NLP implemented by google research team. If we perform   research and need to get a quick summary on a text dataset having  millions of records of what we were reading, BERT summarization would be more helpful for this task. 

Transformer based classification is done by customizing the model where we have changed the last few layers of the for performing the classification based sentiment analysis.

Before even modeling for the considered dataset, in the time-series modeling, we have gone through some of the articles on the web, some of the free video tutorials available and discussed among the teammates on what is the approach to be considered, what is the modeling technique used, etc. After each milestone, we came up with the improvements that have to be made on the model in order to improve its overall performance.

• Why explains the modeling. Explainable ML models. 
• Transformer based Classification & Summarization models: For performing sentiment analysis in the given dataset which is having huge text data, it is difficult to process due to avoidance of system resources, time and cost. For that, we need to summarize the whole text data using BERT extractive summarizer on each record and use that data for sentiment analysis and generate scores for each of them without losing the core content. BERT summarizer was recent revolutions in NLP tasks implemented by  Google, even few other techniques available in the market. Most of the research institutes follow the same kind of architecture to build the summarization models and give best F1 Scores (recall for ROUGE and precision for BLEU).
• Time-series forecasting model: A famous and widely used forecasting method for time-series prediction is the AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA models are capable of capturing different standard temporal structures in time-series data.
• Terminology:

The standard ARIMA models expects 3 arguments i.e. p,d,q as input parameters 

• p is the number of lag observations.
• d is the degree of differencing.
• q is the size/width of the moving average window.

• LSTM model: One of the sought out deep neural network models which uses time series analysis. They are even able to remember the past values and hence, based on the past values, the present stock price values are predicted.

• How: If you would like, you can add a dimension of how. How did it happen? Sometimes, the answer to how can be covered by what, when and where. 

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