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Forecasting Methods Overview
This is an overview of some basic forecasting methods. These basic forecasting methods are broken into two categories of approaches: Quantitative and Qualitative. Quantitative forecasting approaches use historical data and correlative association to make forecasts. Qualitative forecasting approaches look at the opinions of experts, consumers, decision makers, and other stakeholders. This video is about basic forecasting methods and covers 9 of the most common approaches. Avercast forecasting software makes good use of these approaches and is powered by over 200 algorithms.

Forecasting methods made simple - Qualitative and quantitative forecasting
In this video, quantitative and qualitative forecasting methods are discussed. When we have to use qualitative forecasting methods and when we have to use quantitative methods is discussed. Also, a short discussion on the methods of measuring the accuracy of forecast is also done.

Qualitative Forecasting

Forecasting - Qualitative methods
In this video, you will learn about the qualitative methods of forecasting like Delphi method, Sales force forecasting, Executive opinion and Customer survey / market research.

Forecasting 3: Qualitative methods
Adapala Academy


Delphi Technique: The Do Nots and the Why Nots
This lecture describes when to use the Delphi technique, and how to perform a Delphi analysis. This video emphasizes the workflow of the Delphi study, and the assessment of consensus. The lesson is suitable for beginners to the technique or experienced researchers looking to refine their methods.

Delphi Discussions - Debugging Techniques
Jump in and learn various debugging techniques and tools with Zach, Dave, and Jim. Jump in and learn various debugging techniques and tools with Zach, Dave, and Jim. Episodes can be found here, your favorite podcast app, alexa, patreon, and of course on Visit our site for bios about the hosts, show notes, contests, merchandise, and more! Hosts: Dave Nottage, Jim McKeeth, Zach Briggs Codex - Official Codex Site

Quantitative Forecasting

Introduction to Forecasting in Machine Learning and Deep Learning
Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics. In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered. This video was recorded at 2018: If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide . For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide

Predicting with a Neural Network explained
In this video, we explain the concept of using an artificial neural network to predict on new data. We also show how to predict in code with Keras.

Time Series Forecasting

Deep Learning for Time Series Forecasting (COVID-19)
In these live coding sessions we will continue to use [[Deep Neural Network (DNN)|DNNs] to forecast COVID-19 spread and also build out our repository to forecast.

Introduction to Classical Time Series Forecasting
For more details,

Time Series AutoML

Time series met AutoML ([[Colaboratory|Codalab] Automated Time Series Regression) — Denis Vorotyntsev
Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out: How AutoML competition differs from most common Kaggle-alike and why you should try them, Features engineering approach for time-series tasks when you have no idea about domain, Why validation split should emulate train-test split, Why you should always check the code of top participants and how small bugs might drop your score. Presentation

Scaling Machine Learning on Industrial Time Series with Cloud Bigtable and AutoML (Cloud Next '18)
The saying goes that machine learning is about data and algorithms, but mostly data. In a real-world industrial setting, this data is usually messy, error-laden, and inconsistent. This session will present how Cognite is using a wide range of tools in Google Cloud Platform, including Cloud Bigtable, Cloud PubSub, Cloud SQL and AutoML, to address the key pain points in a scalable machine learning workflow: Live data preparation and aggregation, data contextualization at scale, Implementation and operationalization of models.

Scalable AutoML for Time Series Forecasting using Ray
Time Series Forecasting is widely used in real world applications, such as network quality analysis in Telcos, log analysis for data center operations, predictive maintenance for high-value equipment, etc. Classical time series forecasting methods (such as autoregression and exponential smoothing) often involve making assumptions the underlying distribution of the data, while new machine learning methods, especially neural networks often perceive time series forecasting as a sequence modeling problem and have recently been applied to these problems with success (e.g., [1] and [2]). However, building the machine learning applications for time series forecasting can be a laborious and knowledge-intensive process. In order to provide an easy-to-use time series forecasting toolkit, we have applied Automated Machine Learning (AutoML) to time series forecasting. The toolkit is built on top of Ray (a distributed framework for emerging AI applications open-sourced by UC Berkeley RISELab), so as to automate the process of feature generation and selection, model selection and hyper-parameter tuning in a distributed fashion. In this talk we will share how we build the AutoML toolkit for time series forecasting, as well as real-world experience and ‘war stories’ of earlier users (such as Tencent). References: 1. Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu. ‘Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks’ 2. Nikolay Laptev, Slawek Smyl, Santhosh Shanmugam. ‘Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks’ About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.

Timeseries Modelling using AutoML
A time series is simply a series of data points ordered in time. There are plenty of use cases of time series data like sales forecasting, inventory planning, staffing, preventive maintenance, IOT sensors. By using data historical data, businesses can understand trends, make a call on what might happen

Time Series Forecasting - Statistical

Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform well on a wide range of problems, assuming that your data is suitably prepared and the method is well configured. 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet) | Jason Brownlee - Machine Learning Mastery

Modern Time Series Analysis | SciPy 2019 Tutorial | Aileen Nielsen
This tutorial will cover the newest and most successful methods of time series analysis. 1. Bayesian methods for time series 2. Adapting common machine learning methods for time series 3. Deep learning for time series These methods are producing state-of-the-art results in a variety of disciplines, and attendees will learn both the underlying concepts and the Python implementations and uses of these analytical approaches to generate forecasts and estimate uncertainty for a variety of scientific time series.

Time Series Forecasting Theory Part 1 - Datamites Data Science Projects
Looking for #DataScience #Projects? Your can work on above project 'Time Series Forecasting Theory Part 1' Trainer: Mr. Ashok Kumar - Datamites is one of the leading institutes for Data Science courses. You can learn Data Science with Machine Learning, Statistics, Python, Tableau etc,..

Python Live - 1| Time Series Analysis in Python | Data Science with Python Training | Edureka
This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Machine Learning Tutorial Playlist:

Tamara Louie: Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting
Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs. series-forecasting Slides.

Accelerate and Simplify Time Series Analysis and Forecasting with Amazon Forecast
Analyzing and forecasting time series data with traditional methods is a complex and time consuming process that often struggles to produce accurate results for large sets of irregular data by failing to combine it with other relevant independent variables. In this tech talk, we will explore how to accelerate this process by relying on deep learning with the new AI service Amazon Forecast. We will briefly review how the service works and jump into an end-to-end demonstration on a time series use case, diving deep into the steps of the process. Learning Objectives: Discover the options available for time series data analysis and forecasting with AWS, Learn how Amazon Forecast can help you accelerate and simplify complex time series analysis, - Learn how deep learning can increase the accuracy of the forecasts and time series analysis for your data and use cases

ime Series Analysis | Time Series Forecasting | Time Series Analysis in R | Ph.D. (Stanford)
Time Series Analysis is a major component of a Data Scientist’s job profile and the average salary of an employee who knows Time Series is 18 lakhs per annum in India and $110k in the United States. So, it becomes a necessity for you to master time series analysis, if you want to get that high-profile data scientist job. Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity, and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. This full course on Time Series Analysis will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).

Autoregression (AR)

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Auto Regressive Models (AR) | Time Series Analysis | Data Analytics
You will learn the theory behind Auto Regressive models in this video. You need to understand this well before understanding ArIMA, Arch, Garch models Watch all our videos on our video gallery . Visit Contact for study packs & training - Complete Data Science Course :

Auto Regressive Time Series Model in Python
In this tutorial, I will show you how to implement an autoregressive model (AR model) for time series forecasting in Python from scratch. Link to the ADF Test Video :

Moving Average (MA)

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Moving Average Time Series Forecasting with Excel
Forecast Moving Average Time Series Analysis Part I of Introductory Time Series Forecasting Series Introduction to Time Series Forecasting with Moving Averages Part II & III can be found at the links below: Forecasting with Exponential Smoothing and Weighted moving average. Testing the quality of the forecast with Theil's U Introduction to time series forecasting using examples of moving average forecasting. We attempt to forecast the price of Gold using the GLD ETF as a proxy for the price of gold. Includes a discussion of commonly used error measures, mean absolute deviation (MAD), mean squared error (MSE, RMSE) and mean absolute percent error (MAPE). Error measures are used to determine how good your forecast is, in other words, they measure how far off your forecast is on average.

Time Series Analysis using Python | The Moving Average (MA) Model
Data Ranger

Autoregressive Moving Average (ARMA)

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Time Series Analysis and Forecast - Tutorial 3 - ARMA
Iman TSAF GUI Please check out for more tutorials.

Time Series Talk : ARMA Model
The Autoregressive Moving Average (ARMA) model in time series analysis

Autoregressive Integrated Moving Average (ARIMA)

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End to End Time Series Modeling using Auto ARIMA
In this video we will see how we can use Auto ARIMA on new york electricity demand dataset. we will look into detail of preparing, visualizing dataset and then using Auto Arima to model. Further to that we will analyze the forecast and learn various concepts like confidence interval and model diagnostic analysis

Time Series Analysis Using Python | Auto ARIMA
Data Ranger

ARIMA Models
Galit Shmueli Autoregressive integrated moving average (ARIMA) models for forecasting. This video supports the textbook Practical Time Series Forecasting.

ARIMA in Python - Time Series Forecasting Part 2 - Datamites Data Science Projects
Quick simple tutorial on ARIMA time series forecasting in Python. Trainer: Mr. Ashok Kumar -

Seasonal Autoregressive Integrated Moving-Average (SARIMA)

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Time Series Talk : Seasonal ARIMA Model
Intro to the Seasonal ARIMA model in time series analysis.

ARIMA | SARIMA -Time Series Forecasting (Seasonal Auto Regressive Integrated Moving Average)
SARIMA time series forecasting is a very useful modeling tool for forecasting future business sales or other time series forecasts based on historical data. This video covers time series forecasting with both ARIMA and SARIMA models using an open source seasonal data set. The data set can also be downloaded for free on my website

Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX)

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SARIMAX Modeling, Forecast & Simulation Demo
In this tutorial, we demonstrate the steps to construct a seasonal ARIMA with exogenous factors (aka SARIMAX), to forecast and to generate several simulation paths (scenarios) in Microsoft Excel Using only NumXL Functions. For more information (i.e. write-up and example spreadsheet), visit us at:

Time Series Forecasting using ARIMAX and SARIMAX Model
In this Time Series Analysis and Forecasting tutorial I have talked about how you can do the forecasting using ARIMAX and SARIMAX models or algorithms that take the exogenous variable in consideration for applying effect of external factors. Data Set used -

Vector Autoregression (VAR)

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Lecture 5: VAR and VEC Models
This is Lecture 5 in my Econometrics course at Swansea University. Watch Live on The Economic Society Facebook page Every Monday 2:00 pm (UK time) October 2nd - December 2017. In this lecture, I explain how to estimate a vector autoregressive model. We started with explaining the Autoregressive Process to explain the behaviour of a time series and how to present such process in different forms. Then we explained the basic conditions required to estimate a VAR model. The data need to be stationary. You need to choose the optimal lag length. The model must be stable. After estimation, we could test for causality among variables using Granger causality tests. Because VAR models are often difficult to interpret, we can use the impulse responses and variance decompositions. The impulse responses trace out the responsiveness of the dependent variables in the VAR to shocks to the error term. A unit shock is applied to each variable and its effects are noted. Variance Decomposition offers a slightly different method of examining VAR dynamics. They give the proportion of the movements in the dependent variables that are due to their ‘own’ shocks, versus shocks to the other variables. It gives information about the relative importance of each shock to the variables in the VAR. We also covered the concept of co-integration, and how to test for cointegration. Then we discussed the Error Correction Model and Vector Error Correction Model VECM.

Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How
The General Data Protection Regulation (GDPR), which came into effect on May 25, 2018, establishes strict guidelines for managing personal and sensitive data, backed by stiff penalties. GDPR's requirements have forced some companies to shut down services and others to flee the EU market altogether. GDPR's goal to give consumers control over their data and, thus, increase consumer trust in the digital ecosystem is laudable. However, there is a growing feeling that GDPR has dampened innovation in machine learning & AI applied to personal and/or sensitive data. After all, ML & AI are hungry for rich, detailed data and sanitizing data to improve privacy typically involves redacting or fuzzing inputs, which multiple studies have shown can seriously affect model quality and predictive power. While this is technically true for some privacy-safe modeling techniques, it's not true in general. The root cause of the problem is two-fold. First, most data scientists have never learned how to produce great models with great privacy. Second, most companies lack the systems to make privacy-safe machine learning & AI easy. This talk will challenge the implicit assumption that more privacy means worse predictions. Using practical examples from production environments involving personal and sensitive data, the speakers will introduce a wide range of techniques--from simple hashing to advanced embeddings--for high-accuracy, privacy-safe model development. Key topics include pseudonymous ID generation, semantic scrubbing, structure-preserving data fuzzing, task-specific vs. task-independent sanitation and ensuring downstream privacy in multi-party collaborations. Special attention will be given to Spark-based production environments. Talk by Jeffrey Yau.

Volume Weighted Moving Average (VWMA)

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The Moving Average No One Talks About | Volume Weighted Moving Average (VWMA) Trading Strategy
Using moving averages is a common strategy among traders, incorporating them in their stock trading techniques. When trading on line, traders often use the sma simple moving average, ema exponential moving average, 50 week moving average, 20 day moving average, but there are other types of moving averages that are often ignored. In this video you will learn about one of the best moving averages to use when day trading or swing trading the Forex or stock market. In this video you will find out: What is the volume weighted moving average and what are the best moving average settings What are the advantages of trading with the simple and volume weighted moving averages (moving average crossover strategy) How to trade stocks with the volume weighted moving average for day trading or swing trading How to use the volume weighted moving average to trade stocks (for beginners) A simple stock trading strategy using the volume weighted moving average How to increase your chances when taking long and short positions using our simple stock trading system

VWMA Volume Weighted Moving Average Explained
VWMA Volume Weighted Moving Average Explained // Want more help from David Moadel? Contact me at davidmoadel @ Plenty of stock / options / finance education videos here: Disclaimer: I am not licensed or registered to provide financial or investment advice. My videos, presentations, and writing are only for entertainment purposes, and are not intended as investment advice. I cannot guarantee the accuracy of any information provided. retail stock investments, retail stock investor, stock market investing tips, jc penny stock, macys stock, uvxy stock, vxx stock, tvix stock, retail sector investing, FIT GPRO TGT COST M RAD volatility investing, retail sector trading, stock market experts, stock market interview, Stock market volatility lessons for better trading, UVXY VXX TVIX trading options 101, vix trading, vix index, vix volatility, uvxy trading, uvxy stock, uvxy options, uvxy explained, uvxy technical analysis, market volatility, stock market volatility, stock volatility, vix trading strategies, trading vix options, trading vix futures, trading the vix, tvix stock, tvix explained, vxx trading, vxx stock, vxx etf, vxx options, vxx explained, xiv stock, options volatility, options volatility trading, options implied volatility, market volatility explained, shorting the vix, day trading, day trader, day trading strategies, day trading for beginners, day trading stocks, day trading penny stocks, day trading live, day trading setup, day trading academy, day trading options, day trading for dummies, day trading for a living, day trading basics, day trading 101, how to day trade, how to day trade for beginners, how to day trade stocks, how to day trade penny stocks, how to day trade options, how to day trade for beginners, day trader interview, options trading for beginners stock market for beginners stocks for beginners stock investing stock market investing options trading strategies stock trading strategies stock investing penny stocks penny stock trading nasdaq apple twitter education rsi bollinger bands $SPY $QQQ $AAPL $TWTR SPY QQQ AAPL TWTR forex david moadel trading traders investing investors stock charts #vwma #vwmaexplained #davidmoadel

Vector Autoregression Moving-Average (VARMA)

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02417 Lecture 9 part D : VARMA(p,q) as VAR(1) model
Lasse Engbo Christiansen This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018.

Stata Tutorial: Vector Auto-Regression in Stata
Mike Jonas Econometrics Stata commands used to specify and estimate a Vector Auto-regression model and generate Impulse Response Functions given a specific Cholesky Ordering.

Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)

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Multivariate Time Series Analysis with the VARMAX Procedure
Xilong Chen presents using PROC VARMAX for time series analysis SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS

An Introduction to Multiple Time Series Analysis and the VARMAX Procedure
To understand the past, update the present, and forecast the future of a time series, you must often use information from other time series. This is why simultaneously modeling multiple time series plays a critical role in many fields. This paper shows how easy it is to use the VARMAX procedure to estimate and interpret several popular and powerful multivariate time series models, including the vector autoregressive (VAR) model, the vector error correction model (VECM), and the multivariate GARCH model. Simple examples illustrate Granger causality tests for identifying predictive causality, impulse response analysis for finding the effect of shocks, cointegration and its importance in forecasting, model selection for dealing with the trade-off between bias and variance, and volatility forecasting for risk management and portfolio optimization. Presenter: Xilong Chen, Senior Manager, SAS Industry: Financial Services, Banking, Government (Federal State and Local)


How To... Forecast Using Exponential Smoothing in Excel 2013
Learn how to use exponential smoothing to forecast future needs in Microsoft Excel 2013 for Time Series Analysis. Exponential Smoothing forecasts demand in the next time period by taking into account the actual demand in the current period and the forecasted demand for the current time period. Please visit (and subscribe to) my YouTube Channel to view methods of forecasting such as the Simple Moving Average and Weighted Moving Average methods.

Exponential Smoothing Forecast Python and Microsoft Power BI
Learn how to incorporate triple exponential smoothing forecast models in Power BI with the help of Python. Train and test your forecast with these easy to follow steps.

Python Tutorial. Exponential Smoothing Methods
This tutorial has an educational and informational purpose and doesn’t constitute any type of forecasting, business, trading or investment advice. All content, including code and data, is presented with no guarantee of exactness or completeness. Investment Risk and Uncertainty. All tutorial content and conclusions are based on hypothetical historical analysis and not real trading or investing with the possibility of future outliers not previously observed within these time series. Past performance doesn’t guarantee future results. Investment risk and uncertainty can possibly lead to its total loss for unleveraged products and even larger for leveraged ones.

Python Tutorial. Double Exponential Smoothing Methods
This tutorial has an educational and informational purpose and doesn’t constitute any type of forecasting, business, trading or investment advice. All content, including code and data, is presented with no guarantee of exactness or completeness. Investment Risk and Uncertainty. All tutorial content and conclusions are based on hypothetical historical analysis and not real trading or investing with the possibility of future outliers not previously observed within these time series. Past performance doesn’t guarantee future results. Investment risk and uncertainty can possibly lead to its total loss for unleveraged products and even larger for leveraged ones.

Simple Exponential Smoothing (SES)

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Smoothing 4: Simple exponential smoothing (SES)
Galit Shmueli Simple exponential smoothing is a popular data-driven method for forecasting series with no trend and no seasonality. This video supports the textbook Practical Time Series Forecasting.

Time Series Analysis - 6.3.1 - Forecasting Using Simple Exponential Smoothing
Bob Trenwith Practical Time Series Analysis PLAYLIST: 6 - Seasonality, SARIMA, Forecasting 3.1 - Forecasting Using Simple Exponential Smoothing

Holt's Exponential Smoothing

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Smoothing 5: Holt's exponential smoothing
Galit Shmueli Holt's (double) exponential smoothing is a popular data-driven method for forecasting series with a trend but no seasonality. This video supports the textbook Practical Time Series Forecasting.

Forecasting Techniques: Trend-Corrected Exponential Smoothing Method (Holt's Method)
This video illustrates an application of Trend-corrected exponential smoothing technique. The goal in this video is to walk through the steps in this forecasting technique, compute the error measurements, and how to find the best parameter values to obtain the minimum error.

Winter's (Holt-Winter's) Exponential Smoothing (HWES)

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Smoothing 6: Winter's exponential smoothing
Galit Shmueli Winter's (Holt-Winter's) exponential smoothing is a popular data-driven method for forecasting series with a trend and seasonality. This video supports the textbook Practical Time Series Forecasting.

Forecasting in Excel using the Holt-Winter technique

Time Series Forecasting - Deep Learning

Applying deep learning methods like Multilayer Neural Networks and Long Short-Term Memory (LSTM) Recurrent Neural Network models to time series forecasting problems.| Jason Brownlee - Machine Learning Mastery

Forecasting with Neural Networks: Part A
Galit Shmueli What is a neural network, neural network terminology, and setting up a network for time series forecasting This video supports the textbook Practical Time Series Forecasting.

Time Series Data Encoding for Deep Learning, TensorFlow and Keras (10.1)
Time series data is usually represented in the form of sequences when working with Keras and TensorFlow. In this video sequences are introduced for time series prediction.

Dafne van Kuppevelt | Deep learning for time series made easy
PyData Amsterdam 2017 Deep learning is a state of the art method for many tasks, such as image classification and object detection. For researchers that have time series data, but are not an expert on deep learning, the barrier can be high to start using deep learning. We developed mcfly, an open source python library, to help machine learning novices explore the value of deep learning for time series data. In this talk, we will explore how machine learning novices can be aided in the use of deep learning for time series classification. In a variety of scientific fields researchers face the challenge of time series classification. For example, to classify activity types from wrist-worn accelerometer data or to classify epilepsy from electroencephalogram (EEG) data. For researchers who are new to the field of deep learning, the barrier can be high to start using deep learning. In contrast to computer vision use cases, where there are tools such as caffe that provide pre-defined models to apply on new data, it takes some knowledge to choose an architecture and hyperparameters for the model when working with time series data. We developed mcfly, an open source python library to make time series classification with deep learning easy. It is a wrapper around Keras, a popular Python library for deep learning. Mcfly provides a set of suitable architectures to start with, and performs a search over possible hyper-parameters to propose a most suitable model for the classification task provided. We will demonstrate mcfly with excerpts from (multi-channel) time series data from movement sensors that are associated with a class label, namely activity type (sleeping, walking, climbing stairs). In our example, mcfly will be used to train a deep learning model to label new data. Code for This Video Course Homepage

Jeffrey Yau: Time Series Forecasting using Statistical and Machine Learning Models | PyData NYC 2017
PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.

Joe Jevnik - A Worked Example of Using Neural Networks for Time Series Prediction
PyData New York City 2017 Slides: Most neural network examples and tutorials use fake data or present poorly performing models. In this talk, we will walk through the process of implementing a real model, starting from the beginning with data collection and cleaning. We will cover topics like feature selection, window normalization, and feature scaling. We will also present development tips for testing and deploying models.

How to Use TensorFlow for Time Series
We're going to use TensorFlow to predict the next event in a time series dataset. This can be applied to any kind of sequential data. Code for this video

Demand Forecasting

Demand Forecasting Using AI and Machine Learning (AI For Business Episode 1)
In this video, we will explore how Machine Learning is used for demand forecasting. Enjoy the video and please like, subscribe and turn on the notifications. In this video, I will start with identifying the challenge, followed by exploring the solution, then providing background of the process and the technology behind it, and finally how it can be applied in the enterprise together with some examples.

Samsung Cello Demand Sensing: An AI-enabled demand forecasting tool
By creating weekly sellout forecasting based on Cello Supply Chain Management (SCM) platform technologies and Samsung SDS' own big data analytics engine Brightics AI, Cello Demand Sensing helps you forecast demand accurately. Find out more at Subscribe to our channel: