Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

This is a project on "Stock-Market-Analysis-And-Forecasting-Using-Deep-Learning" using Pytorch, python, deep learning, gru, plotly

This repo demonstrates the development of a real-time data pipeline designed to ingest, process, and analyze stock market data. Using cutting-edge tools like Apache Kafka, PostgreSQL, and Python, the pipeline captures stock data in real-time and stores it in a robust data architecture, enabling timely analysis and insights.

Stock Market Analysis using Python

This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.

Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

- Performed stock market analysis of technology company’s stocks. - Used pandas to get stock information and to visualize different aspects of stock and performed risk analysis of the stock based on its previous performance history.

The aim of the project was to extract information about various technology stocks mainly - Google, Apple, Microsoft and Amazon from the online stock trading sites - Yahoo Finance and to visualize different aspects of the stocks like the Adjusted Closing Prices, Volumes of stocks traded on a particular day, moving averages of the closing price-to get a basic idea of which way the price is moving by cutting down noise from the data and the daily returns on the stocks. Correlation plots were created for the daily percentage return and Closing prices of the stocks to check how correlated two stocks are. It was obvious that all technology stocks are positively correlated but few like Amazon and Microsoft were highly correlated with each other. The information gathered on daily percentage returns was further used for Risk Analysis by calculating the Expected Return (Average / mean return of the stock) and standard deviation (measurement of Risk -> Greater the std. dev. greater is the risk and vice versa). A scatter plot was created for comparing the Expected return of stocks to its risk. This helped in visualizing the risk factor of various stocks (stocks with high standard deviation and low return).

One year stock market analysis of top Indian Stocks

NSE Stock Market Analyser in a python based tool which makes use of "nsetools" and requests api to fetch stock data in json format. In the initial version, I am supporting a basic option chain analysis, which gives the max call point, max put point and max pain point of a derivative. I will also be supporting Put/Call ratio, nth order support points and nth order pain points, and many more advanced features

A Python project for stock market analysis and prediction using EDA, Linear Regression, correlation analysis, and data visualization techniques.

Stock Market analysis on Bursa Malaysia using Python

Stock Market prediction using Decision Tree Regressor , classifier and linear Regression. Built with python, Decision Tree classifier Model in Jupyter-lab.

Exploratory data analysis of stock market data using Python

This is a repository for implementing various algorithmic trading and quantitative analysis techniques for the Indian Stock Market using Python. It includes a variety of strategies ranging from simple moving averages to more advanced techniques like machine learning-based algorithms.

Perform technical analysis on stock market data in Python using several technical indicators such as Moving Averages, Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD).

JPMorgan Chase has traders in all the major financial centers and creates a marketplace for asset classes around the globe for our investor clients. Trading teams are organized by asset class: Equities (stocks) Commodities Credit (debt and bonds) Currency & Emerging Markets Public Finance (Government bonds) Interest rates Securitized Products For this project, a trader from the equities team (publicly listed company stocks) has requested functionality be added to their dashboard to allow them to input specific information so they can monitor a new trading strategy. In order to do this, you’ll need to set up your system so you can interface with the relevant financial data feed, make the required calculations and then present this in a way that allows the traders to visualize and analyze this data in real time. The visualization of charts and data analysis our trader’s see is all built on JPMorgan Chase's own open sourced software called Perspective. You’ll learn how to implement this to facilitate the trader’s requested changes and deliver actionable insights. You’ll have to gain an understanding of the user requirements and then build something that meets those requirements. 1: Interface with a stock price data feed Interface with a stock price data feed and set up your system for analysis of the data Financial Data Python Git Basic Programming 2: Use JPMorgan Chase frameworks and tools Implement the Perspective open source code in preparation for data visualization React Typescript Web Applications 3: Display data visually for traders Use Perspective to create the chart for the trader’s dashboard Technical Communication Financial Analysis Web Applications

No description available

Jupyter Notebooks with different purposes: Social Network WebScrapping, ETL, Selenium WebDriver for Web Testing, Automation using Python, Data Wrangling, Data Transformation, Data Cleaning, Stock Market Analysis, APIs, Machine learning Algorithms, etc...

This project aims to perform a detailed data-driven analysis of the S&P 500 stock market using historical stock price data from the period 2014 to 2017. Leveraging Python and essential data science tools, the project uncovers key financial insights through statistical analysis, exploratory data analysis (EDA), and visualizations.

This repository contains code for analyzing the correlation between financial news sentiment and stock market movements. Using NLP for sentiment analysis and statistical techniques for correlation, the project aims to enhance predictive analytics in financial forecasting. Python, TA-Lib, PyNance, and GitHub Actions are utilized.

This repository contains the Codes that are written in python programming language. In this the codes are mainly based upon the analysis of the OHLC Data of the stock market by using various indicators that are used in stock market.

The Time Series Analysis - Stocks application is a web-based tool for analyzing stock market data. Built using Python, Streamlit, and Plotly, this application allows users to visualize historical stock prices through different types of charts.

A Python-based tool to analyze stock performance and provide insights using historical data, technical indicators, and market trends. The tool fetches data for a given stock ticker using the Yahoo Finance API (`yfinance`) and generates a comprehensive analysis report using a custom AI model (`ollama`).

Projects made using R and Python on Stock Market Analysis, Football Analytics, Website Analytics, etc. I now realize that I could have done a lot more productive things. However, I am happy and that's all that matters.

In finance stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This project is about the prediction of a stock using Machine Learning. This analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. This study uses a machine learning technique called Support Vector Machine (SVM), Random Forest Classifier and Linear Regression methods to predict stock prices for the large and small capitalizations. The study aims to analyse the analysis of NSE listed FMCG companies in India with a sample size of four companies for a period from 2000 to 2018. From the Economic analysis, it is found that Gross Domestic Product, Inflation, Interest rates, Exchange rate and Consumer Confidence has impact on FMCG sector.

# Forecasting Stock Market Prices It is a **Time Series** dataset.A time series is simply a series of data points ordered in time.In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. ## PROBLEM STATEMENT: Our Aim is to create a model that can forecast the future stock price based on the model training and provided dataset. ### Data We will be using a [Huge stock market dataset](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs) from the Kaggle platform which has a very good collection of datasets.The file we will be using is present in following directory in the dataset zip file input\Data\Stocks\gs.us.txt The data is presented in CSV format as follows : Date, Open, High, Low, Close, Volume, OpenInt. Features: - Date - Open - High - Low - Close - Volume - OpenInt Note that prices have been adjusted for dividends and splits. ### LICENSE OF DATASET : [LICENSE](https://creativecommons.org/publicdomain/zero/1.0/) ### Requirements You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html) If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. This project requires **Python** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org/) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) - [statsmodels](https://www.statsmodels.org/stable/) ### Run In a terminal or command window, navigate to the top-level project directory `STOCK MARKET FORECASTING/` (that contains this README) and run one of the following commands: ipython notebook Forecasting_Stock_Market_Prices_task.ipynb or jupyter notebook Forecasting_Stock_Market_Prices_task.ipynb This will open the Jupyter Notebook software and project file in your browser. ### Steps : 1. Importing Libraries 2. Exploring the Dataset 3. Exploratory Data Analysis > * Univariate Analysis 4. Data Preprocessing 5. Model Building > * AUTOREGRESSIVE MODEL > * MOVING AVERAGE MODEL 6. Evaluation > * MEAN SQUARE ERROR > * MEAN ABSOLUTE ERROR > * ROOT MEAN SQUARE ERROR 7. Conclusion