bootstrapped prediction intervals

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Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data Science | Simplilearn

This Time Series Analysis (Part-2) in R tutorial will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text.

Link to Time Series Analysis Part-1: https://www.youtube.com/watch?v=gj4L2isnOf8

You can also go through the slides here: https://goo.gl/9GGwHG

A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.

Below topics are explained in this “ Time Series in R Tutorial “ –

1. Introduction to ARIMA model
2. Auto-correlation & partial auto-correlation
3. Use case – Forecast the sales of air-tickets using ARIMA
4. Model validating using Ljung-Box test

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Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth .53 Billion by 2019
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4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT

The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.

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5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields

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Multivariate Time Series Prediction with LSTM and Multiple features (Predict Google Stock Price)

Build a Artificial Neural Network (ANN) with Long-Short Term Memory (LSTM) to predict value which can be impacted by multiple different features.

In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let’s say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc.

Link to github notebook: https://github.com/vb100/multivariate-lstm/blob/master/LSTM_model_stocks.ipynb

The video has 3 parts:
– Part 01. Data pre-processing (4:11)
| Step 01: Read data.
| Step 02: Data pre-processing (shaping and transformations).

– Part 02. Create a LSTM model and train it. (10:39)
| Step 03: Building-up the LSTM based Neural Network.
| Step 04: Start training.

– Part 03. Make future predictions. (13:50)
| Step 05: Make predictions for future date.
| Step 06: Visualize the predictions.

In this tutorial I used Tensorflow 1.15.0 and Keras 2.3.1

Download data from: https://finance.yahoo.com/quote/GOOG/history (check 1:59 in video).

This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned.

My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset).

Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects. TIme Series forecasting with LSTM is the good choice if you want to manipulate with multiple different data features and see which ones has impact to predictions and which ones do not.

If you are interested how to run Tensorboard on this LSTM Keras model, check this tutorial: https://youtu.be/-9-Hy5dWKLE

Sorry for video quality. There were some unexpected issues with resolution.
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Time Series Analysis (Georgia Tech) – 5.2.2 – State Space Modelling – Prediction and Estimation

Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech
Unit 5: Other Time Series Methods
Part 2: Multivariate Time Series Modelling
Lesson: 2 – State Space Modelling – Prediction and Estimation
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Creating a time series forecast when the series has a trend. Use excel’s „slope“ and „intercept“ commands to estimate the equation for a line and use it to forecast future values. Create a historical forecast using the line and compare it to actual data to evaluate the likely precision of your forecast.

Cryptocurrency funds Costs Prediction: Ml Investments Method (XGBOOST)

Cryptocurrency Price Prediction: Machine Learning Trading Algorithm (XGBOOST)

ARTIST CREDIT
Evgeny Rodygin – https://www.artstation.com/erodygin
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If you want to utlilise the power of machine learning to predict price in cryptocurrency you need to be paying attention to the right things.

Today we’ll be looking at the XGBOOST algorithm and how it has been applied in other industries and also how you can use it in the crypto space.

You’ll see here the link to data and machine learning is fundamental to the bigger picture.

Understand the opportunity in this space and how you can use this to your advantage.

#cryptocurrency #machinelearning #cryptoalgorithm

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