Trending February 2024 # Working With Stock Market Time Series Data Using Facebook Prophet # Suggested March 2024 # Top 9 Popular

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This article was published as a part of the Data Science Blogathon

 Time-series data analysis consists of working with this data and gather actionable insight from the data. Examples of time-series data would be Prices of Petrol with time, Rainfall over time, Stock prices over time.

Importance of working with Time Series data

Time series data helps in various business cases, like predicting sales over time, forecasting visitors to a website, or the number of users. Such things help in optimizing various aspects of an organization. Often, the data points taken in Time series analysis have internal relations or some unseen structure like a trend or seasonal variations. These are often not visible with just a look at the data. A detailed study is needed in those cases. The observed data can be used for various purposes and can be modeled according to our needs.

Time Series Data

Applications of Time Series Analysis

There are various applications of Time Series Analysis and Time Series Models, some of them are :

Sales Forecasting

Economic Forecasting

Weather Forecasting

Stock Market Analysis

Product Demand Analysis

Population Growth/ Census

and others.

Today, we will be looking at the stock market analysis part. Stock Markets are always uncertain and erratic, it takes years of study and a lot of experience to understand the trend of the market. As the stock market involves a lot of work, a large number of participants and numerous factors make predictions about stock market trends very tough. The stock price of a company fluctuates a lot during the day, let alone the whole week. All these things make decisions very tough to make, in the case of the stock market. So, let us try working with Facebook Prophet, and see if it solves our problems.

Facebook Prophet is an open-source forecasting method implemented in Python and R. It provides automated forecasts. Prophet is used in many applications relating to time series data and to gather sample time forecast data. In the case of such models, getting exact future data is never possible, but we can somehow get the future trend.

You can install Prophet using :

pip install prophet Getting some Stock Market stock market data

We shall be web scraping Facebook’s stock data using Yahoo Finance. Yahoo Finance makes it very easy to extract stock data, hence my choice here. If necessary you can make any other choice. With Yahoo Finance, we get the data as simple as using dataframes, which can be easily worked in Python.

Let us proceed with the code, I will leave a link to the complete code at the end of the blog.

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) from prophet import Prophet

Some important libraries are imported, more libraries to be imported.

import warnings warnings.filterwarnings('ignore') # Hide warnings import datetime as dt import pandas as pd import pandas_datareader.data as web import numpy as np import matplotlib.pyplot as plt import seaborn as sns import matplotlib.dates as mdates

Now all important libraries are imported.

#getting the stock data using yahoo finance start = dt.datetime(2010, 1, 1) end = dt.datetime(2024,1,1) df = web.DataReader("FB", 'yahoo', start, end) # Collects data #prices in USD

We took data from 2010 to the end of 2024. This is marked by using (2010,1,1) at the start date-time and using (2024,1,1) at the end date. This way, taking the time frame is done and it is quite easy to proceed with Yahoo Finance.

And stock code for Facebook is FB and yahoo for Yahoo finance. A point to be noted is that one can use any Company stock data or any other type of time-series data.



This is how the data looks like, you can find more about these stock terminologies by checking out this article. We are interested in the Date and Adjusted Close values here. As, Date will count as the Time Series data index and Adj Close will be our time series data. In the data, stock values are mentioned as the closing price and the adjusted closing price. The closing price is the raw price, which is just the cash value of the last transacted price before the closure of the stock market for the day. The adjusted closing price factors in anything that might affect the stock price after the market closes.

Now, to get the date as a column.

df.reset_index(inplace=True) data=df[["Date","Adj Close"]] data=data.rename(columns={"Date": "ds", "Adj Close": "y"}) #now it is usable for FB Prophet data.head()

Now, the data can be used in FB Prophet.

Now the data has length 911.

We split the data into train and test parts.

df_train=data[0:500] df_test=data[500:911]

After this, we create the Prophet model. And use the training data to train the model.

m = Prophet() m.fit(df_train)

Now, let us make some predictions.

future = m.make_future_dataframe(periods=411) forecast = m.predict(future) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

This is what is generated. A data frame with the time index and other values.

“ds” indicates the Date data, “yhat” indicates the predicted time series data. “yhat_lower” and “yhat_upper” indicate the probable lower and upper limit of how much the value can go.

 Let us plot the data.

fig1 = m.plot(forecast)

The plot looks as such.

We had data till 2014-06, after that the data generated is generated by the Prophet model. Let us plot the other components.

According to the model, the trend of the curve is almost upward, and that has been the case for FB stock. Facebook has been a profitable company, and hence stock prices rise up.

Let us compare it to the real stock data of FB in that time period.

We can see that, for the case of Facebook stock, there was an increase in the period. We can assume that FB prophet cannot obviously predict accurate stock values, but can predict an overall trend in time series data.

Conclusion

With better research and better tuning, more accurate results can be predicted. FB Prophet can be used to do efficient Time Series analysis, as it provides fast and simple to use methods for this purpose.

Get the full code on Kaggle.

Stock Price Prediction with FB Prophet

Connect with me on Linkedin

Thank You.

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Real Time Data In Power Bi Using Pubnub

In this post I’m going to look at getting real time data (RTD) into Power BI using a real time messaging service called PubNub.

This is intended for use with the Power BI online service, not Power BI Desktop.

Power BI provides a few different ways to get RTD : Push Data, Streaming Data and PubNub Streaming.

Push Data

With this method, data is pushed, or sent, to Power BI and stored in a database that Power BI automatically creates.

Because the data is stored in a database, you can create reports using this data, as well as seeing the new data update in real time.

Streaming Data

Streaming data is also pushed to Power BI but by default the data is not stored in a database.

You can tell Power BI to store this streamed data in which case you can run reports and analyse the data stored in the Power BI database.

But if you create the dataset as a ‘normal’ streamed dataset, Power BI only retains the data as long as it needs to display it on a tile. You can’t create reports for this data.

PubNub Streaming

PubNub is a data streaming network (DSN) that provides a real time messaging service.

Put another way, it’s a high speed, low latency network that is built to allow you to easily send data from one place to another.

As with a lot of things that can be explained in a short, simple sentence, it is a very powerful concept.

Say you have an IoT device like a temperature sensor, or a GPS enabled vehicle, or maybe you’ve written an app that monitors your website’s uptime, anything that can record or generate data and has access to the internet, can use PubNub to send that data to anybody or anything that you want to send it to.

As we are streaming data to Power BI from PubNub, there is no database created in Power BI to store the PubNub data. We can visualize the data in tiles, but we can’t run reports against the data.

Pushing Data to Power BI Datasets

It’s worth mentioning at this point that there are a few ways to actually push your data into Power BI.

You can write your own applications (programs) that use the Power BI REST API.

This will require a good knowledge of programming and is no easy task.

If you use Azure Stream Analytics (ASA) you can configure Power BI to receive data from ASA but this is also a daunting task for the non-developer.

The easiest approach is to use PubNub. It’s pleasantly uncomplicated to do and although it does require some programming knowledge, or at least the will to give it a go, with the sample files I provide, hopefully you’ll be able to get your own test system up and running in no time.

First Things First – Setup a PubNub Account

To use PubNub you’ll need an account with them. They offer a free account for anyone interested in testing things out, so go and sign up now.

Once you are logged in, the first thing you should see is this which is telling you to go and get your API keys. You’ll need these to send and receive messages (data).

Please note that I have removed part of my Publish key to prevent naughty people sending data through my account. You should treat your own pub key carefully and don’t give it to anyone you don’t want sending data through your PubNub account.

When you have your API keys, you’re ready to start sending some PubNub messages.

Sending Data via PubNub

The idea is that you create a ‘channel’ along which you can send data.

A channel is just a name you give to something in PubNub. You don’t need to worry about what it really is or how it works, PubNub does all this for you. You’ll see later how easy it is to setup and use.

Anybody or anything that wants to receive this data can connect to the channel and listen for your messages, so long as you give them the subscribe key.

The data you send can be any JSON serializable data, which means you can send numbers, strings, arrays or objects.

You can send binary data (images, sounds) or any UTF-8 character, either single or multi-byte.

All of this requires a little programming but PubNub provides sample code and SDK’s (software development kits) for over 70 programming languages.

So it doesn’t matter if you prefer Python, PHP, JavaScript, or something else. At least one of the languages you use is supported with sample code supplied.

I’m going to use JavaScript as it will run in your browser and makes demonstrating this much easier.

The Publisher

The code that sends the data, I’m calling the publisher. Remember the publisher can be anything. The computer monitoring the engine in your car. Your alarm system at home. If it has some data and can access the internet, you just need to hook it up with some code and you can send that data down a PubNub channel.

For my sample application I’m going to get the price in USD of Bitcoin, Ethereum and Litecoin from Crypto Compare, and send these prices down my channel where I’ll read them with another piece of code I’ll call my subscriber.

To begin with we need to insert into our code the keys we got earlier from our PubNub account.

The publishKey allows us to create a channel and send messages. The subscribeKey allows us to receive messages. The subscriber part of the code only needs the subscribe key.

A function called mainApp() calls the Crypto Compare website and gets the prices in USD for the crypto currencies. It does this every 2000 milliseconds. You can change this value if you wish.

When we have these prices, this code in the processRequest() function sends the prices down the channels.

There’s a channel for each crypto currency; bitcoin-feed, ether-feed and litecoin-feed. The act of sending data down a channel will create that channel if it doesn’t already exist. You don’t need to explicitly create a channel.

That is the whole thing. The JavaScript will continue to load prices every 2 seconds and sent the prices down the respective channels.

The Subscriber

Enter your subscribe key in the JavaScript (or whatever language you are using).

Tell the code what channels you want to receive data from by subscribing to them

Then listen for data and write some code to deal with the data when it arrives

I’ve written some HTML and CSS to make the prices look nice when they are displayed in the browser, but you can make it as simple or as fancy as you like.

At it’s most basic you can just write data to the JavaScript console in your web browser (see the line of code in the red box above) just to prove that the data is being received.

What we are aiming for is to receive this data in Power BI so you don’t need to go nuts with your data presentation in the browser.

Get The Files

Both the publisher and subscriber files can be downloaded. These are HTML files and can be edited with a text editor – don’t use Word.

Enter your email address below to download the files.

By submitting your email address you agree that we can email you our Excel newsletter.

Please enter a valid email address.

Getting the data Into Power BI

Now we have our publisher running, we can go back to Power BI and start receiving the data.

If you haven’t already got a workspace then create one so you can keep things neatly organised.

If everything is OK and the publisher code is running, Power BI will be able to connect to the channel and receive some data whch it will present like this.

If there’s a problem, Power BI won’t receive any data and it will give you an error. If that happens, check that you have entered the sub key and channel name correctly and that the publisher code is running in your browser.

Creating a Dashboard

With the streaming dataset created we can now use it in a dashboard.

I’ll use a Card visualization, and there’s only one field to display

You should now have a tile showing real time updates for the price of Bitcoin in USD.

Conclusion

Using PubNub is a lot easier than writing code to use the Power BI API to get real time data into your dashboards.

Even if you only have a little bit of knowledge of how to program it’s worth giving it a go to see what you can do.

Check with your data provider to see if they publish their data to PubNub.

Arima Model For Time Series Forecasting In Python

A popular and widely used statistical method for time series forecasting is the ARIMA model. Exponential smoothing and ARIMA models are the two most widely used approaches to time series forecasting and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data. Before we talk about the ARIMA model, let’s talk about the concept of stationarity and the technique of differencing time series.

Stationarity

A stationary time series data is one whose properties do not depend on the time, That is why time series with trends, or with seasonality, are not stationary. the trend and seasonality will affect the value of the time series at different times, On the other hand for stationarity it does not matter when you observe it, it should look much the same at any point in time. In general, a stationary time series will have no predictable patterns in the long-term.

What is ARIMA?

ARIMA is an acronym that stands for Auto-Regressive Integrated Moving Average. It is a class of model that captures a suite of different standard temporal structures in time series data.

In this tutorial, We will talk about how to develop an ARIMA model for time series forecasting in Python.

An ARIMA model is a class of statistical models for analyzing and forecasting time series data. It is really simplified in terms of using it, Yet this model is really powerful.

ARIMA stands for Auto-Regressive Integrated Moving Average.

The parameters of the ARIMA model are defined as follows:

p: The number of lag observations included in the model, also called the lag order.

d: The number of times that the raw observations are differenced, also called the degree of difference.

q: The size of the moving average window, also called the order of moving average.

Steps to Use ARIMA Model

A linear regression model is constructed including the specified number and type of terms, and the data is prepared by a degree of differencing in order to make it stationary, i.e. to remove trend and seasonal structures that negatively affect the regression model.

Visualize the Time Series Data

Visualize the Time Series Data involves plotting the historical data points over time to observe patterns, trends, and seasonality.

Identify if the date is stationary

Identify if the data is stationary involves checking whether the time series data exhibits a stable pattern over time or if it has any trends or irregularities. Stationary data is necessary for accurate predictions using ARIMA, and various statistical tests can be employed to determine stationarity.

Plot the Correlation and Auto Correlation Charts

To plot the correlation and auto-correlation charts in the steps of using the ARIMA model online, you analyze the time series data. The correlation chart displays the relationship between the current observation and lagged observations, while the auto-correlation chart shows the correlation of the time series with its own lagged values. These charts provide insights into potential patterns and dependencies within the data.

Construct the ARIMA Model or Seasonal ARIMA based on the data

To construct an ARIMA (Autoregressive Integrated Moving Average) model or a Seasonal ARIMA model, one analyzes the data to determine the appropriate model parameters, such as the order of autoregressive (AR) and moving average (MA) components. This step involves selecting the optimal values for the model based on the characteristics and patterns observed in the data.

Let’s Start

import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline

In this tutorial, I am using the below dataset.

df=pd.read_csv('time_series_data.csv') df.head() # Updating the header df.columns=["Month","Sales"] df.head() df.describe() df.set_index('Month',inplace=True) from pylab import rcParams rcParams['figure.figsize'] = 15, 7 df.plot()

if we see the above graph then we will able to find a trend that there is a time when sales are high and vice versa. That means we can see data is following seasonality. For ARIMA first thing we do is identify if the data is stationary or non – stationary. if data is non-stationary we will try to make them stationary then we will process further.

Let’s check that if the given dataset is stationary or not, For that we use adfuller.

from statsmodels.tsa.stattools import adfuller

I have imported the adfuller by running the above code.

test_result=adfuller(df['Sales'])

To identify the nature of data, we will be using the null hypothesis.

H0: The null hypothesis: It is a statement about the population that either is believed to be true or is used to put forth an argument unless it can be shown to be incorrect beyond a reasonable doubt.

H1: The alternative hypothesis: It is a claim about the population that is contradictory to H0 and what we conclude when we reject H0.

#Ho: It is non-stationary

#H1: It is stationary

We will be considering the null hypothesis that data is not stationary and the alternate hypothesis that data is stationary.

def adfuller_test(sales): result=adfuller(sales) labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations'] for value,label in zip(result,labels): print(label+' : '+str(value) ) if result[1] <= 0.05: print("strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data is stationary") else: print("weak evidence against null hypothesis,indicating it is non-stationary ") adfuller_test(df['Sales'])

After running the above code we will get P-value,

ADF Test Statistic : -1.8335930563276237 p-value : 0.3639157716602447 #Lags Used : 11 Number of Observations : 93

Here P-value is 0.36 which is greater than 0.05, which means data is accepting the null hypothesis, which means data is non-stationary.

Let’s try to see the first difference and seasonal difference:

df['Sales First Difference'] = df['Sales'] - df['Sales'].shift(1) df['Seasonal First Difference']=df['Sales']-df['Sales'].shift(12) df.head()

# Again testing if data is stationary adfuller_test(df['Seasonal First Difference'].dropna()) ADF Test Statistic : -7.626619157213163 p-value : 2.060579696813685e-11 #Lags Used : 0 Number of Observations : 92

Here P-value is 2.06, which means we will be rejecting the null hypothesis. So data is stationary.

df['Seasonal First Difference'].plot()

I am going to create auto-correlation :

from pandas.plotting import autocorrelation_plot autocorrelation_plot(df['Sales']) plt.show()

from statsmodels.graphics.tsaplots import plot_acf,plot_pacf import chúng tôi as sm fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(211) fig = sm.graphics.tsa.plot_acf(df['Seasonal First Difference'].dropna(),lags=40,ax=ax1) ax2 = fig.add_subplot(212) fig = sm.graphics.tsa.plot_pacf(df['Seasonal First Difference'].dropna(),lags=40,ax=ax2)

# For non-seasonal data #p=1, d=1, q=0 or 1 from statsmodels.tsa.arima_model import ARIMA model=ARIMA(df['Sales'],order=(1,1,1)) model_fit=model.fit() model_fit.summary()

Dep. Variable:D.SalesNo. Observations:104Model:ARIMA(1, 1, 1)Log-Likelihood-951.126Method:css-mleS.D. of innovations2227.262Date:Wed, 28 Oct 2023AIC1910.251Time:11:49:08BIC1920.829Sample:02-01-1964HQIC1914.536 – 09-01-1972    RealImaginaryModulusFrequencyAR.12.3023+0.0000j2.30230.0000MA.11.0000+0.0000j1.00000.0000

df['forecast']=model_fit.predict(start=90,end=103,dynamic=True) df[['Sales','forecast']].plot(figsize=(12,8))

import chúng tôi as sm model=sm.tsa.statespace.SARIMAX(df['Sales'],order=(1, 1, 1),seasonal_order=(1,1,1,12)) results=model.fit() df['forecast']=results.predict(start=90,end=103,dynamic=True) df[['Sales','forecast']].plot(figsize=(12,8))

from pandas.tseries.offsets import DateOffset future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)] future_datest_df=pd.DataFrame(index=future_dates[1:],columns=df.columns) future_datest_df.tail() future_df=pd.concat([df,future_datest_df]) future_df['forecast'] = results.predict(start = 104, end = 120, dynamic= True) future_df[['Sales', 'forecast']].plot(figsize=(12, 8))

Frequently Asked Questions

Q1. What is ARIMA Model or algorithm?

A. ARIMA (Autoregressive Integrated Moving Average) is a statistical model used to analyze and forecast time series data. It combines autoregressive (AR) and moving average (MA) components, along with differencing operations, to capture the underlying patterns and predict future values based on the historical behavior of the data.

Q2. What is ARIMA model formula?

A. The general formula for an ARIMA(p, d, q) model is: Y(t) = c + φ₁Y(t-1) + φ₂Y(t-2) + … + φₚY(t-p) + θ₁ε(t-1) + θ₂ε(t-2) + … + θ_qε(t-q) + ε(t), where Y(t) represents the time series at time t, c is a constant term, φ represents autoregressive coefficients, θ represents moving average coefficients, ε(t) is white noise, and p, d, and q are the orders of autoregressive, differencing, and moving average components, respectively.

Conclusion

Time Series forecasting is really useful when we have to take future decisions or we have to do analysis, we can quickly do that using ARIMA, there are lots of other Models from we can do the time series forecasting but ARIMA is really easy to understand.

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Nubia Redmagic 7 Series With An Under

One of the selling points of this series is the under-display camera technology. However, only the Nubia REDMAGIC 7 Pro comes with this feature. Also, this is the first gaming smartphone to use an under-display camera technology.

Under-display camera technology

The Pro model of this series comes with the latest under-display camera (UDC) technology. It also comes with a tripod-shaped pixel arrangement and wave-shaped electrode wiring. Furthermore, this smartphone arrives with seven layers of high-transparency materials as well as a built-in UDC Pro screen display chip. While this smartphone promises a premium gaming experience, it also offers a completely full-screen display. 

Display

The Nubia REDMAGIC 7 series comes with a 6.8 FHD+ AMOLED display with a screen ratio of 20:9. This display also supports a resolution of 2400*1080, and a screen-to-body ratio of 91.28%. The impressive colour hits a depth of 10bit and full DCI-P3 colour. This smartphone display also hit a peak brightness of 700 nits with a contrast ratio as high as 1000000:1. However, the Pro model brightness is 600 nits. Although not the highest in the industry, even in daylight it is ultra-clear. Furthermore, this display has SGS certification for eye care. This display offers the ultimate visual experience for all types of gaming and entertainment.

Software

These smartphones come with a custom operating system (REDMIMAGIC OS 5.0) on top of Android 12. The new operating system has intelligent scheduling of CPU, GPU, and memory. This enables it to respond faster when in use. It also responds faster during booting, application startup, game loading, and touch screen response. Furthermore REDMAGIC OS 5.0 improves the Touch Choreographer (TC) feature. This ensures that the frame rate is more stable throughout the user experience.

Hardware & battery

The REDMAGIC 7 series comes with an optimized 4nm Snapdragon 8 Gen 1 processor. This chip delivers a top-notch gaming experience as well as top image quality and smoothness. The graphical capabilities are also excellent.

While the REDMAGIC 7 PRO comes with a 5000 mAh battery, the REDMAGIC 7 comes with a dual-cell of 4500 mAh battery. The Pro model supports up to 135W quick charging and it gets a full charge in only 15 minutes. As for the regular REDMAGIC 7, it supports 120W air-cooled fast charging and delivers a full charge in just 17 minutes. With this charging capacity, users only need a short break to get a full charge before getting back into the game.

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Nubia REDMAGIC 7 series gaming features Shoulder triggers

Shoulder triggers are slowly becoming a must-have feature for gaming smartphones. The REDMAGIC 7 and REDMAGIC 7 Pro both have 500Hz touch sampling shoulder triggers. According to the company, the response speed of these triggers is as low as 7.4ms. This makes the devices extremely responsive giving the player a faster and more accurate gaming experience. Furthermore, these triggers use a five-channel high-performance custom IC. This makes its operation smoother and better. Another interesting aspect of these triggers is their custom feature. Players can custom the triggers to carry out multiple complex actions.

Nubia claims that the triggers can withstand over two million finger presses which is more than enough for any gamer. The triggers come with an ergonomic design and also support sweat resistance. This makes the triggers more comfortable and accurate throughout gaming time.

Magic GPU image enhancement

To get an ultra-fast reading and writing output, these gaming smartphones use a new Magic GPU image enhancement and Magic Write. The Magic Write increases the speed of application installation by 30% relative to SSD.

Game Space

There is a switch on the side of the REDMAGIC 7 and REDMAGIC 7 Pro. If the user flips the switch, it takes the user into the REDMAGIC Game Space. In this space, users can collect games and tweak the performance of the gaming smartphone. You can save battery, block notification, optimize shoulder triggers, and so on in the Game Space.

Heat dissipation and cooling system

After the display and processor, the heat dissipation and cooling system is probably the most important aspect of any gaming device. These smartphones come with a new heat dissipation cooling system. They upgrade from ICE 7.0 to ICE 8.0 cooling system. The nine-layer structure of the cooling system ensures that this gaming smartphone is the first with a cooling material area of up to 41279mm2 and a 4124mm2 super large VC cooling plate.

Relative to the previous generation, the latest ICE 8.0 cooling system includes a built-in turbofan that can reach up to 20,000rpm with a core heat source temperature lower than 3°C.  The canyon air duct for the REDMAGIC 7 and REDMAGIC 7 Pro also has a second air inlet, which helps to increase airflow by 35%.

There is also a 45° angle opening on the back that forms double air inlets, achieving a temperature drop of 2.4°C. As for the built-in turbofan, it uses an energy-efficient brushless motor that is very quiet at only 28 decibels. This built-in turbofan helps to cool the CPU’s core temperature by 16°C, ensuring continuous and stable output of the core performance.

The new Turbo Cooler, features an innovative turbo centrifugal fan with a heat-dissipating capacity of 17%, a maximum cooling of two degrees lower, and is 3-decibels quieter than competitors. Furthermore, with the release of the REDMAGIC 7 series in China, nubia offers a new Magnetic Cooler accessory with a cooling fan for iPhone users.

AI & 5G

The Nubia REDMAGIC 7 series features Mora, REDMAGIC’s new virtual AI Assistant and animated mascot. These smartphones also come with an independent gaming chip “Red Core 1”. This chip enhances the shoulder triggers, vibration, game lighting effect, and sound effect. With an LPDDR5 elevating the mobile gaming experience on 5G and enabling a transfer rate of 6400 Mbps/51.2 GB, the new REDMAGIC 7 series is a smartphone to try out.

Selling points

The major selling points of the Nubia REDMAGIC 7 series includes

 500Hz Dual Pro Shoulder Triggers

Game Boost Switch button and Game Space

charged in 15 minutes

4500 mAh battery supporting up to 120W quick charging, and fully charged in 17 minutes

165W charger in the box.

Seventh generation ultra-thin screen fingerprint sensor

Triple camera setup with Neovision AI photography

Better audio experience with gaming: 3 mics, dual smart PA, DTS Ultra X, and a 3.5mm headphone jack

Classic gaming aesthetic design with RGB light strip

Availability & Price

The Nubia REDMAGIC 7 comes in Cyber Neon, Night Knight, and Deuterium Transparent Edition versions. The Cyber Neon and Night Knight includes cool lighting effects on the REDMAGIC logo light at the back and RGB lights. The Deuterium Transparent Edition includes a REDMAGIC logo light and RGB lights within the famed built-in turbofan. You can clearly see this via the transparent rear.

The REDMAGIC 7 Pro comes in Cybern Neon, Polar Black Night, and Deuterium Blade Transparent editions. The Polar Black Night and Cyber Neon editions have RGB lighting effects on the back of the device (REDMAGIC logo light).  The Deuterium Blade Transparent edition lights just like the regular model.

These smartphones are on pre-sale in China will commence outright sales on February 21st. As for the global model, it will be announced on February 22 and the sales will commence on March 10th.

Prices in China REDMAGIC 7:

Cyber Neon & Night Knight 8GB + 128GB – 3999¥ ($631)

Deuterium Transparent Version 12GB + 256GB – 4899¥ ($773)

REDMAGIC 7 Pro:

Cybern Neon & Polar Black Night 12GB + 128GB – 4799¥ ($757)

Deuterium Blade Transparent 12GB + 256GB – 5299¥ ($836)

Deuterium Blade Transparent 18GB + 1TB – 7499¥ ($1,183)

Facebook Wins Standoff With Australian Government

Facebook announced an agreement with the Australian government to pay Australian news organizations for their news articles. The government compromised with Facebook, agreeing that Facebook retains the right to make decisions about news on Facebook in exchange for supporting Australian news organizations.

News links and posts are returning to Australian users of Facebook.

Facebook Rescinds Australian News Ban

Last week Facebook announced the dramatic banning of news for Australian members of Facebook. The reason was to avoid being governed by a law that would force Facebook to pay for Australian news organizations for sharing links on Facebook and profiting from those links.

Facebook called the proposed law a failure to understand the relationship between Facebook and news organizations.

Today Campbell Brown, VP, Global News Partnerships announced that Facebook had reached an agreement.

According to the announcement:

“After further discussions with the Australian government, we have come to an agreement that will allow us to support the publishers we choose to, including small and local publishers.

We’re restoring news on Facebook in Australia in the coming days.

Going forward, the government has clarified we will retain the ability to decide if news appears on Facebook so that we won’t automatically be subject to a forced negotiation.

“Media Conglomerates” a Reference to Rupert Murdoch?

One of the biggest media conglomerates in Australia is Rupert Murdoch’s News Corp, which has been alleged in news articles to be the one behind the Australian government attempt to shake down Google and Facebook on behalf of Australian news organizations.

According to the Sydney Morning Herald:

“News Corp has successfully lobbied the government. A compulsory code being created to make Google and Facebook pay for the use of news content is just one example of an issue News Corp lobbied hard for. “

So when Facebook made reference to resisting “media conglomerates, that may have been a reference to Rupert Murdoch and News Corp and the alleged backroom lobbying to extract money from Google and Facebook.

The law itself was meant to benefit local journalism. But an Australian government analysis of the law found that as it was written the news organizations were free to spend the money in any manner they deemed fit.

According to the analysis:

“…it remains to be seen whether any benefit gained by the registered news businesses is used to support public interest journalism.”

The proposed law allowed news organizations to pocket the money without in any way benefiting public interest journalism.

Facebook Wins

The fight between Australia and Facebook was important because it could have served as a blueprint for other countries to extract money from Facebook. The new agreement keeps Facebook from being subjected to automatic negotiations.

Citation

Read the official Facebook Statement

Navigating Facebook With These Keyboard Shortcuts

Keyboard Shortcuts For Easily Navigating Facebook

Here are the key combinations that you need to know for your specific web browser. Where it says “#”, it is a placeholder that you have to replace with the shortcut mentioned in the later part of this paragraph. Internet Explorer for PC – Alt + # + Enter Chrome for Mac – Control + Option + # Chrome for PC – Alt + # Firefox for Mac – Control + Option + # Firefox for PC – Shift + Alt + # Safari for Mac – Control + Option + # Here are the numbers that you can use in place of “#” in the above key combinations. Help – 0 Home – 1 Timeline – 2 Friends – 3 Inbox – 4 Notifications – 5 Settings – 6 Activity Log – 7 About – 8 Terms – 9

Newsfeed Shortcuts Web Messenger Shortcuts Conclusion

Facebook’s built-in web messenger also comes with shortcuts to make your messaging experience faster and easier.– Control + g– Control + q– Control + delete– Control + j– Control + m– Control + i– Control + u

If you did not know about these shortcuts you should learn them now, as they will make your Facebook experience much faster and more convenient than ever.

Mahesh Makvana

Mahesh Makvana is a freelance tech writer who’s written thousands of posts about various tech topics on various sites. He specializes in writing about Windows, Mac, iOS, and Android tech posts. He’s been into the field for last eight years and hasn’t spent a single day without tinkering around his devices.

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