Shou Zi Chew

 Shou Zi Chew (born 1 January 1983) is a Singaporean businessman and entrepreneur who has served as chief executive officer of TikTok since 2021.^[1][2][3]

Early life and education[edit]

Chew was born on 1 January 1983 in Singapore.^[4] He was born into a working class family, his father worked in construction while his mother was a bookkeeper.^[5]

Upon his graduation from Hwa Chong Institution,^[5] Chew went on to serve his National Service (NS), where he serve as a commissioned officer in the Singapore Army.^[5]

After his active military service, Chew went on to study at University College London,^[6] where he graduated in 2006 with a Bachelor of Economics degree.^[7] He subsequently completed a Master of Business Administration degree at Harvard Business School in 2010.^[4] While studying at Harvard, Chew completed a summer internship at Facebook.^[8]

Chew is fluent in English, Chinese, and Malay languages

Average Auto Loan Rates in December 2022

 

卡尔加里

 这么棒的城市，房价如何？

众所周知，加拿大一些城市以超高的房价著称，比如多伦多和温哥华。近日，房地产网站 Zoocasa 发布了一份“北美最佳和最差的住房负担能力城市”的研究报告。报告中，该公司研究了北美 35 个城市的经济适用房以及当地的收入情况。当中，卡尔加里在“最能负担得起住房城市 ” 排名中上榜第一！它是唯一一个进入前五名的加拿大城市。

Zoocasa 报告称，卡尔加里的房价中位数为 $30 万加币，想在这座城市买得起房子年收入需要在大约 $57,037 加币。

与加拿大其他大城市如多伦多或温哥华不同，卡尔加里人的实际收入中位数（$97,334 加币）高于购买房屋所需的收入，这意味着居民的收入还有约为 $40,297 加币的盈余。

很高兴地看到，有一座城市成为了房价可负担性最好的地方，那就是卡尔加里！

6408, 302 Skyview Ranch Drive NE

$219,000

Property Summary for 6408, 302 Skyview Ranch Drive NE

Type

 

Single Family

Sub-Type

 

Condo/Strata

Title

 

Condo/Strata

MLS® Number

 

25371140

Year Built

 

2016

Association Fee

 

$270.22 CAD

Neighborhood

 

Skyview Ranch

Postal Code

 

T3N0P5

Rooms Information

Description for 6408, 302 Skyview Ranch Drive NE

Welcome to #6408, 302 Skyview Ranch Drive NE, a fantastic top floor unit, featuring 2 bedrooms, an open plan, a four piece bath, and lots of extra features. The kitchen is highlighted by stainless steel appliances and quartz counters. The flooring in the main is cork, which is super durable, and the east facing balcony overlooking a green space is the perfect spot for a morning coffee. There is one title heated underground parking stall, and extra storage too. Skyview Ranch has everything you need in a community, with restaurants, groceries, and everything else just a short distance away, including the LRT line and transit. For more info, and to see our 360 tour, please click the links below. (id:1937)

4641 128 Avenue NE 1411, Calgary

 $193,000

Property Summary for 4641 128 Avenue NE

Type

 

Residential

Sub-Type

 

Apartment

Style

 

High-Rise (5+), Apartment, Residential, High Rise (5+ stories)

Title

 

Fee Simple

MLS® Number

 

A2029785

Year Built

 

2019

Full Baths

 

1

Stories

 

6

Parking Info

 

1

Association Fee

 

$212.25 CAD

Neighborhood

 

Skyview Ranch

Postal Code

 

T3N 1T2

Rooms Information

Description for 4641 128 Avenue NE

Affordable condo unit situated in a family-friendly community of Skyview Ranch. Close to schools, shops, airport and easy access to downtown. Low condo fee ($212.25/mth). Condo fee includes heat, water, sewer, trash, snow removal, common area maintenance, professional management, and reserved fund contribution. The unit comes with 1 bedroom and a den with closet (the den is big enough to accomodate a double size bed and can be used as a secondary bedroom), one bath, and one heated UG titled parking stall. Located on the fourth floor, the condo unit has been upgraded with soft close cabinet doors and drawers, contemporary melamine cabinets finished with modern colour, granite in kitchen and bathroom counter, stainless appliances, firm fit luxury vinyl plank flooring throughout (only bedroom and den has carpet), a full size front load stacked washer and dryer, and a sprinkler in every room. A daycare facility is conveniently located on the main floor of building 1000 and a fitness center in building 2000. Building is still under the National Home Warranty program. Exceptional value. Please contact your realtor for a showing today!

What is Zebra BI?

 

What is Zebra BI?

Zebra BI is a complete toolkit for creating understandable and actionable reports. It consists of three visuals that cover all business reporting needs: Tables, Charts, and Cards. 

Each functionality of Zebra BI is designed to support best practices in modern BI, such as showing variances and comparisons, adding dynamic comments to explain the context, using advanced charts, and much more. By leveraging the unified design of all elements, you'll be able to create clear and consistent reports that everyone in the company will understand. 

If you approach Zebra BI with the mindset of data storytelling, you'll be able to learn it very quickly. And as a result, you'll end up creating reports that your colleagues and managers will love. 

Understanding ARIMA Models for Machine Learning

 

Here is the article.

A simple introduction to understanding autoregressive integrated moving averages

If you are among the 50% of Americans who own stock, I am sure you have had some sleepless nights thinking about the future price of your investments. You may try and calm your fears by reading predictions by economists and other investment professionals -- but how do they come up with their forecasts? One way is by using autoregressive integrated moving average (ARIMA) models.

What is an Autoregressive Integrated Moving Average?

Autoregressive Integrated Moving Average (ARIMA) models have many uses in many industries. It is widely used in demand forecasting, such as in determining future demand in food manufacturing. That is because the model provides managers with reliable guidelines in making decisions related to supply chains. ARIMA models can also be used to predict the future price of your stocks based on the past prices. Do note, that although they might help you predict changes to the S&P 500 Index’s price over time, I am so sorry to say, it won’t help you earn quick money by predicting when viral stocks like Gamestop (GME) will shoot up next time.

That’s because ARIMA models are a general class of models used for forecasting time series data. ARIMA models are generally denoted as ARIMA (p,d,q)  where p is the order of autoregressive model, d is the degree of differencing, and q is the order of moving-average model. ARIMA models use differencing to convert a non-stationary time series into a stationary one, and then predict future values from historical data. These models use “auto” correlations and moving averages over residual errors in the data to forecast future values.

Potential pros of using ARIMA models

• Only requires the prior data of a time series to generalize the forecast.
• Performs well on short term forecasts.
• Models non-stationary time series.

Potential cons of using ARIMA models

• Difficult to predict turning points.
• There is quite a bit of subjectivity involved in determining (p,d,q) order of the model.
• Computationally expensive.
• Poorer performance for long term forecasts.
• Cannot be used for seasonal time series.
• Less explainable than exponential smoothing.

How to build an ARIMA model

Let’s say you want to predict a company’s stock price with an ARIMA model. First, you will have to download the company’s publicly available stock price over the last few -- let’s say ten -- years. Once you have this data, you are now ready to train the ARIMA model. Based on trends in the data, you will choose the order of differencing(d) required for this model. Next, based on autocorrelations and partial autocorrelations, you can determine the order of regression (p) and order of moving average (q). An adequate model can be selected using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), maximum likelihood, and standard error as performance metrics.

Understanding how the ARIMA model works

As stated earlier, ARIMA(p,d,q) are one of the most popular econometrics models used to predict time series data such as stock prices, demand forecasting, and even the spread of infectious diseases. An ARIMA model is basically an ARMA model fitted on d-th order differenced time series such that the final differenced time series is stationary.

A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. A stationarized series is relatively easy to predict --you simply predict that its statistical properties will be the same in the future as they have been in the past!

To understand how an ARIMA model functions, there are three terms within the name that you will need to better understand:

• AutoRegressive - AR(p) is a regression model with lagged values of y, until p-th time in the past, as predictors. Here, p = the number of lagged observations in the model, ε is white noise at time t, c is a constant and φs are parameters.

Conclusion

The ARIMA methodology is a statistical method for analyzing and building a forecasting model which best represents a time series by modeling the correlations in the data. Owing to purely statistical approaches, ARIMA models only need the historical data of a time series to generalize the forecast and manage to increase prediction accuracy while keeping the model parsimonious.

Despite being parsimonious, there are multiple potential disadvantages to using ARIMA models. Most important of them stems from the subjectivity involved in identifying p and q parameters. Although autocorrelation and partial autocorrelations are used, the choice of p and q depend on the skill and experience of the model developer. Additionally, compared to simple exponential smoothing and the Holt Winters method, ARIMA models are more complex and thus, have lower explanatory power.

Lastly, similar to all forecasting methods, by being backward looking, ARIMA models are not good at long term forecasts and are poor at predicting turning points. They can also be computationally expensive.

Thus, ARIMA models can be easily and accurately used for short-term forecasting with just the time series data, but it can take some experience and experimentation to find an optimal set of parameters for each use case.

Power BI | MAQ software | Four forecasting models | include Linear Regression, ARIMA, Exponential Smoothing, and Neural Network

 

 years ago

Forecast Using Multiple Models by MAQ Software lets you implement four different forecasting models to learn from historical data and predict future values. The forecasting models include Linear Regression, ARIMA, Exponential Smoothing, and Neural Network.

Learn more in our new introduction video below. #powerbi

Here is the link. 

MAQ Software | Enterprise Data & Analytics Services

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Amazon ends its charity donation program AmazonSmile after other cost-cutting efforts

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 The slider revolution plugin for wordpress allows you to create some really amazing sliders for your wordpress website. IT has more than 200+ templates to help get you started and after watching this tutorial you can expect to learn how to create your own wordpress slider with the slider revolution plugin

Here are some of the timestamps that i talked about in the video TimeStamps: Creating A Slider: 3:59 Template Library: 34:50 Using The Addons: 41:41 General Settings: 50:34 Advanced Features: 1:01:27

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How To Make a WordPress Website with Elementor - 2022

How To Make a WordPress Website with Elementor - 2022

 Timestamps for this video

00:00 Intro 07:10 Get Hosting And Domain 13:47 General Settings 18:15 Make Pages And Menu 24:25 Design Website With Elementor Page Builder 38:00 Import Starter Templates 45:20 Import Demo Template 50:12 Design Tip 54:03 Theme Customizer 01:01:00 Switching WordPress Themes 01:04:30 Mobile Optimization 1:10:25 Elementor Advanced Features 1:19:45 Design Tip 1:23:22 Bonus Section! 1:25:37 Closing

Update from CEO Andy Jassy on Amazon’s operating plan and additional role eliminations

 As our internal businesses evaluated what customers most care about, they made re-prioritization decisions that sometimes led to role reductions, sometimes led to moving people from one initiative to another, and sometimes led to new openings where we don’t have the right skills match from our existing team members. This initially led us to eliminate 18,000 positions (which we shared in January); and, as we completed the second phase of our planning this month, it led us to these additional 9,000 role reductions (though you will see limited hiring in some of our businesses in strategic areas where we’ve prioritized allocating more resources).

Clean the home | Keep minimum

How to Decide What to Keep or Toss When Decluttering

By 

ELIZABETH LARKIN

 

Updated on 09/12/22

Every item you own—from shoes and small appliances to paper—takes up valuable real estate in your home. It's challenging to decide what to keep or throw out when going through the process of decluttering.

Here are seven questions to ask yourself every time you need help deciding what to toss or keep.