Tag: Time Series Analysis

Course content

• Introduction
• Deep learning environment setup
• Deep learning dataset preparation using ArcGIS Pro
• Open source solution for data preparation (geotile)
• Image classification
• Deep learning object detection
• Image segmentation (Binary class)
• Image segmentation (Multi-class)
• Landslide detection
• Time Series Analysis with LSTM
• Flood mapping
• End to End Deep Learning and Google Earth Engine

Course content

• Welcome
• Getting Set Up
• Time Series Basics
• Exponential Smoothing and ETS Methods
• ARIMA
• Vector Autoregression (VAR, VMA, VARMA)
• Machine Learning Methods
• Deep Learning: Artificial Neural Networks (ANN)
• Deep Learning: Convolutional Neural Networks (CNN)
• Deep Learning: Recurrent Neural Networks (RNN)
• VIP: GARCH
• VIP: AWS Forecast
• VIP: Facebook Prophet
• Setting Up Your Environment FAQ
• Extra Help With Python Coding for Beginners FAQ
• Effective Learning Strategies for Machine Learning FAQ
• Appendix / FAQ Finale

Duration

21 hours (usually 3 days including breaks)

Overview

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Course Outline

Introduction and preliminaries

• Making R more friendly, R and available GUIs
• Rstudio
• Related software and documentation
• R and statistics
• Using R interactively
• An introductory session
• Getting help with functions and features
• R commands, case sensitivity, etc.
• Recall and correction of previous commands
• Executing commands from or diverting output to a file
• Data permanency and removing objects

Simple manipulations; numbers and vectors

• Vectors and assignment
• Vector arithmetic
• Generating regular sequences
• Logical vectors
• Missing values
• Character vectors
• Index vectors; selecting and modifying subsets of a data set
• Other types of objects

Objects, their modes and attributes

• Intrinsic attributes: mode and length
• Changing the length of an object
• Getting and setting attributes
• The class of an object

Arrays and matrices

• Arrays
• Array indexing. Subsections of an array
• Index matrices
• The array() function
• The outer product of two arrays
• Generalized transpose of an array
• Matrix facilities
  • Matrix multiplication
  • Linear equations and inversion
  • Eigenvalues and eigenvectors
  • Singular value decomposition and determinants
  • Least squares fitting and the QR decomposition
• Forming partitioned matrices, cbind() and rbind()
• The concatenation function, (), with arrays
• Frequency tables from factors

Lists and data frames

• Lists
• Constructing and modifying lists
  • Concatenating lists
• Data frames
  • Making data frames
  • attach() and detach()
  • Working with data frames
  • Attaching arbitrary lists
  • Managing the search path

Data manipulation

• Selecting, subsetting observations and variables          
• Filtering, grouping
• Recoding, transformations
• Aggregation, combining data sets
• Character manipulation, stringr package

Reading data

• Txt files
• CSV files
• XLS, XLSX files
• SPSS, SAS, Stata,… and other formats data
• Exporting data to txt, csv and other formats
• Accessing data from databases using SQL language

Probability distributions

• R as a set of statistical tables
• Examining the distribution of a set of data
• One- and two-sample tests

Grouping, loops and conditional execution

• Grouped expressions
• Control statements
  • Conditional execution: if statements
  • Repetitive execution: for loops, repeat and while

Writing your own functions

• Simple examples
• Defining new binary operators
• Named arguments and defaults
• The ‘…’ argument
• Assignments within functions
• More advanced examples
  • Efficiency factors in block designs
  • Dropping all names in a printed array
  • Recursive numerical integration
• Scope
• Customizing the environment
• Classes, generic functions and object orientation

Graphical procedures

• High-level plotting commands
  • The plot() function
  • Displaying multivariate data
  • Display graphics
  • Arguments to high-level plotting functions
• Basic visualisation graphs
• Multivariate relations with lattice and ggplot package
• Using graphics parameters
• Graphics parameters list

Time series Forecasting

• Seasonal adjustment
• Moving average
• Exponential smoothing
• Extrapolation
• Linear prediction
• Trend estimation
• Stationarity and ARIMA modelling

Econometric methods (casual methods)

• Regression analysis
• Multiple linear regression
• Multiple non-linear regression
• Regression validation
• Forecasting from regression

How to create a Neural Network with Python

How to prepare data for Time Series Analysis

How to evaluate Machine Learning models

How to perform a reliable backtest with Python

Requirements

• Basic knowledge of Python

Description

Enter the world of Neural Networks and Financial Forecasting with this free course.

Can you forecast the returns of your favorite stock using Machine Learning?

Artificial Intelligence is certainly changing the world:

From the way we get our content, autonomous driving, medical advances to art creation.

Financial Machine Learning is one of the industries with a bigger impact on these technologies, from Roboadvisors to Algorithmic Trading.

Most recommendations made by firms are based on Artificial Intelligence nowadays, rendering most conventional analysts useless.

The same happens for traders, not many years ago trading was done manually, currently a huge share of the market is being traded by AI.

These advances have changed the game, gaining insight with edges the human eye can’t see anymore.

While the biggest financial institutions have been trading using Artificial Intelligence for years, most retail traders don’t know how to use nor benefit from them, we are here to change that.

Roll up your sleeves with this hands-on project where you are going to learn by doing and interacting with code, completely from scratch.

In this course you are going to learn how to:

• Download Historical Data from your code, automatically.
• Prepare your data with the most suitable indicators.
• How to label and prepare data to feed our model.
• Prepare a Neural Network.
• Evaluate models.
• Backtest your ML Model.
• Create accurate stock forecasts.

We hope you enjoy this course.

Genbox Trading

Who this course is for:

• traders and coders who wants to use Machine Learning

Course content