Hudson  Kunde

Hudson Kunde

1590895500

Forecasting Apple Stock Prices Using LSTM’s and Tensorflow

In this post, we will build a LSTM Model to forecast Apple Stock Prices, using Tensorflow!
Stock Prices Prediction is a very interesting area of Machine Learning. Personally, I always have interest in the applications of this field.
Machine Learning became very useful to the Stock Market Forecasting over the last years, and today, many investment companies are using Machine Learning to make decisions in the Stock Market.
And unlike what many think, we don’t need to have a amazing Machine Learning Model to really make money.

#machine-learning #tensorflow #stock-market #stock-price #lstm

What is GEEK

Buddha Community

Forecasting Apple Stock Prices Using LSTM’s and Tensorflow
Hudson  Kunde

Hudson Kunde

1590895500

Forecasting Apple Stock Prices Using LSTM’s and Tensorflow

In this post, we will build a LSTM Model to forecast Apple Stock Prices, using Tensorflow!
Stock Prices Prediction is a very interesting area of Machine Learning. Personally, I always have interest in the applications of this field.
Machine Learning became very useful to the Stock Market Forecasting over the last years, and today, many investment companies are using Machine Learning to make decisions in the Stock Market.
And unlike what many think, we don’t need to have a amazing Machine Learning Model to really make money.

#machine-learning #tensorflow #stock-market #stock-price #lstm

Dominic  Feeney

Dominic Feeney

1619240400

How to Predict Stock Prices with LSTM

How to Predict Stock Prices with LSTM

A Practical Example of Stock Prices Predictions with LSTM using Keras TensorFlow

In a previous post, we explained how to predict stock prices using machine learning models. Today, we will show how we can use advanced artificial intelligence models such as the Long-Short Term Memory (LSTM). In the previous post, we have used the LSTM models for Natural Language Generation (NLG) models, like the word-based and the character-based NLG models.The LSTM ModelLong short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning having feedback connections. Not only can process single data points such as images, but also entire sequences of data such as speech or video. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, anomaly detection, time series analysis, etc.

he LSTM models are computationally expensive and require many data points. Usually, we train the LSTM models using GPU instead of CPU. Tensorflow is a great library for training LSTM models.

#lstm #tensorflow #stock-price-prediction #prediction-markets #python

Predicting Stock Prices Using An LSTM Model

1. Introduction

1.1. Time-series & forecasting models

Traditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no time dimension in the data.

Time-series forecasting models are the models that are capable to predict future values based on previously observed values. Time-series forecasting is widely used for non-stationary data. **Non-stationary data **are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these 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.

A time series is a sequence of observations taken sequentially in time.

Image for post

An example of a time-series. Plot created by the author in Python.

Observation: Time-series data is recorded on a discrete time scale.

**Disclaimer **(before we move on): There have been attempts to predict stock prices using time series analysis algorithms, though they still cannot be used to place bets in the real market. This is just a tutorial article that does not intent in any way to “direct” people into buying stocks.


2. The LSTM model

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points (e.g. images), but also entire sequences of data (such as speech or video inputs).

LSTM models are able to store information over a period of time.

In order words, they have a memory capacity. Remember that LSTM stands for Long Short-Term Memory Model.

This characteristic is extremely useful when we deal with Time-Series or Sequential Data. When using an LSTM model we are free and able to decide what information will be stored and what discarded. We do that using the “gates”. The deep understanding of the LSTM is outside the scope of this post but if you are interested in learning more, have a look at the references at the end of this post.

#lstm #forecasting #machine-learning #recurrent-neural-network #stock-market #deep learning

Christa  Stehr

Christa Stehr

1597067873

Advertising Stocks Face New, Major Challenge With Apple’s iOS 14

This earnings season promises to be a wild ride across the tech sector as initial impact from the coronavirus will be reported while a few outliers will seem impervious. Ad-tech stocks are especially vulnerable to other sectors with Google expected to have its first decline year-over-year in company history. Facebook boycotts that came late in June could affect future quarters. We’ve seen Twitter report 23% lower revenue and entertain new methods of monetization. However, these well-known risks will be rivaled if not exceeded by the effects of the lesser-known announcement from Applelast month in regards to the required opt-in for the ID for Advertisers (IDFA).

The IDFA is a number tied to the device that allows ad exchanges to track user interactions and behavior. The primary function is very similar to cookies in that it helps ad companies store data profiles and preferences for personalized messaging, regardless of which device you are logged into. In addition to targeting, the IDFA also helps with attribution and measurement.

If you’ve never heard of the IDFA or are not aware that a number is assigned to your iOS device to help track you, it’s because this has been opt-out in the past and been hidden inconspicuously in your Settings. In the upcoming release of iOS 14 in September, Apple will make this an opt-in for every single application. This means a message will appear for every application using a mobile device ID asking for permission.

Image for post

Pictured above:_ Apple will require opt-in permission to track for displaying targeted ads, sharing device location, sharing a list of emails, ad IDs or other IDs used to retarget and/or placing a third-party SDK in the app that combines user data from your app with user data to target advertising. See the full list here on _Developer.Apple.Com

Below, I go over the background that led to Apple’s decision and the public companies this might affect. As noted below, this should affect companies who offer mobile targeting, such as Google, Facebook, Twitter/MoPub and The Trade Desk. In the interim, it could also affect any applications that use aggressive growth tactics. This list is harder to identify, but Uber and Lyft, for example, are known for spending heavily on user acquisition to drive installs.

For instance, Snap beat on revenue recently yet some of this beat came from direct response ads, such as TikTok driving user acquisition on mobile. In this case, there will be less information about who is taking an action if Snap users do not opt-in on the warning screen. Twitter, as well, pointed towards direct response ads holding up revenue during the pandemic while brand ads have weakened. Yet again, direct response has a new and very serious obstacle.

The silence on this topic from financial analysts on Twitter’s earnings call when many ad-tech companies including two of the world’s most valuable companies rely on the IDFA for a sizable chunk of revenue is odd to say the least. AppsFlyer places mobile app install spend at $80 billion in 2020 and estimates this will reach $118 billion by 2022. This is compared to the total mobile advertising industry worth $241 billion in 2019 and $368 billion in 2022.

The changes will not take effect until September with most devices running the iOS update by October, so no financial impact will be seen until Q4. However, if brand ad spend remains low from the pandemic, and direct response campaigns will now be blind due to an aggressive move against mobile ad targeting, then investors should expect a significant shift in the ad industry by the latter part of the year.

I first covered this in October for MarketWatch with the article, “Governments can’t stop Google and Facebook but Apple can.” The changes to the IDFA are being done under a privacy guise, however, it could be an attempt for Apple to reclaim valuable revenue streams from its ecosystem as iPhone penetration is maxed out. How this would work is not evident right now but its unlikely that a $118 billion market in iOS app install spend has gone unnoticed.

Apple and its operating system is the most important governor in the mobile industry with two-thirds of mobile acquisition spend compared to Android’s one-third. If Apple is playing the long-game on reclaiming iOS attribution and measurement to generate revenue, then Google, Facebook, Twitter, Snap and The Trade Desk have plenty to worry about as Apple can undeniably claim this turf.

#tech-stocks #stocks #apple #apple-ios-14

Hubify Apps

Hubify Apps

1614420140

Back In Stock Notification App for Your Shopify Store

The last thing you want to do is to dissatisfy your customers. It is quite disappointing for online shoppers to want to purchase a product and they end up discovering that it is out of stock.

One thing that is common among Shopify stores is that they usually experience stockouts. A stockout occurs when inventory gets finished. If customers want to handle issues concerning stock outs effectively, then, they should use Shopify product back-in-stock alerts App.

What can back in stock alerts help you do? It can help customers notify shoppers when products are available if they subscribe to it using the back in stock notification app.

Learn More : https://hubifyapps.com/back-in-stock-notification-app/

#back in stock notification app #back in stock alert #in stock alert #in stock #back in stock #stock alert app