Angela  Dickens

Angela Dickens

1598812860

Comparing the performance of forecasting models: Holt-Winters vs ARIMA

This is the third in a series of articles I am writing on time series forecasting models. The first article was a real-world application of time series forecasting that I did with US housing market data, with the purpose of demonstrating how forecasting is implemented and interpreted. In the second article, I outlined 5 simple steps to build a forecasting model aiming at beginners who have never done forecasting before. In today’s article, I’m going to show how two different techniques can be applied to the same dataset and how to evaluate and compare their performance.

Among the many different forecasting techniques out there, I’ve picked 2 models to evaluate: ARIMA and Holt-Winters exponential smoothing. Before going any further I’ll briefly describe what they are and how they differ conceptually.

ARIMA (Autoregressive Integrated Moving Average):ARIMA is arguably the most popular and widely used statistical technique for forecasting. As the name suggests, ARIMA has 3 components: a) an _Autoregressive _component models the relationship between the series and its lagged values; b) the Moving Average component predicts future value as a function of lagged forecast errors; and c) the Integrated component makes the series stationary.

ARIMA model — represented as _ARIMA(p, q, d) _— takes the following parameters:

  • p that defines the number of lags;
  • d that specifies the number of differences used; and
  • q that defines the size of moving average window

**Holt-Winters: **it is another suit of techniques that also uses historical values. However, a key distinguishing feature is the so-called “exponential smoothing”.

If decomposed, a time series will disaggregate into 3 components: trend, seasonality, and white noise (i.e., random data points). For forecasting purposes, we can predict the predictable components (i.e., trend and seasonality), but not the unpredictable terms which occur in a random fashion. Exponential smoothing can handle this kind of variability within a series by smoothing out white noise. A Moving Average can smooth training data, but it does so by taking an average of past values and by weighting them equally. On the other hand, in Exponential Smoothing, the past observations are weighted in an exponentially decreasing order. Meaning, most recent observations are given higher weights than far-away values.

#machine-learning #arima #data-science #time-series-forecasting #forecasting #big data

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Comparing the performance of forecasting models: Holt-Winters vs ARIMA
Angela  Dickens

Angela Dickens

1598812860

Comparing the performance of forecasting models: Holt-Winters vs ARIMA

This is the third in a series of articles I am writing on time series forecasting models. The first article was a real-world application of time series forecasting that I did with US housing market data, with the purpose of demonstrating how forecasting is implemented and interpreted. In the second article, I outlined 5 simple steps to build a forecasting model aiming at beginners who have never done forecasting before. In today’s article, I’m going to show how two different techniques can be applied to the same dataset and how to evaluate and compare their performance.

Among the many different forecasting techniques out there, I’ve picked 2 models to evaluate: ARIMA and Holt-Winters exponential smoothing. Before going any further I’ll briefly describe what they are and how they differ conceptually.

ARIMA (Autoregressive Integrated Moving Average):ARIMA is arguably the most popular and widely used statistical technique for forecasting. As the name suggests, ARIMA has 3 components: a) an _Autoregressive _component models the relationship between the series and its lagged values; b) the Moving Average component predicts future value as a function of lagged forecast errors; and c) the Integrated component makes the series stationary.

ARIMA model — represented as _ARIMA(p, q, d) _— takes the following parameters:

  • p that defines the number of lags;
  • d that specifies the number of differences used; and
  • q that defines the size of moving average window

**Holt-Winters: **it is another suit of techniques that also uses historical values. However, a key distinguishing feature is the so-called “exponential smoothing”.

If decomposed, a time series will disaggregate into 3 components: trend, seasonality, and white noise (i.e., random data points). For forecasting purposes, we can predict the predictable components (i.e., trend and seasonality), but not the unpredictable terms which occur in a random fashion. Exponential smoothing can handle this kind of variability within a series by smoothing out white noise. A Moving Average can smooth training data, but it does so by taking an average of past values and by weighting them equally. On the other hand, in Exponential Smoothing, the past observations are weighted in an exponentially decreasing order. Meaning, most recent observations are given higher weights than far-away values.

#machine-learning #arima #data-science #time-series-forecasting #forecasting #big data

Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Jamison  Fisher

Jamison Fisher

1623226129

Time Series Analysis Using ARIMA Model With Python

Time series_ is a sequence of time-based data points collected at specific intervals of a given phenomenon that undergoes changes over time. In other words, time series is a sequence taken at consecutive equally spaced points in the time period._

As a example, we can present few time series data sets in different domains such as pollution levels, Birth rates, heart rate monitoring, global temperatures and Consumer Price Index etc. At the processing level, above datasets are tracked, monitored, down sampled, and aggregated over time.

There are different kind of time series analysis techniques in the big data analytical field. Among them few are,

  • Autoregression (AR)
  • Moving Average (MA)
  • Autoregressive Moving Average (ARMA)
  • Autoregressive Integrated Moving Average (ARIMA)
  • Seasonal Autoregressive Integrated Moving-Average (SARIMA)

ARIMA Model

ARIMA Model is simple and flexible enough to capture relationship we would see in the data and It aims to explain the autocorrelation between the data points using past data. We can decompose the ARIMA model as follow to grab the key elements of it.

  • **AR: _Auto regression. _**This is a model that uses the dependent relationship between the data and the lagged data.
  • **I:_ Integrated. _**The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
  • **MA: _Moving average. _**A model that uses the relationship between the observations and the residual error from the moving average model applied to lagged observations.

Dataset Explanation

Exploratory Analysis

#python #time-series-analysis #pandas #forecasting #arima #time series analysis using arima model with python

Joe  Hoppe

Joe Hoppe

1595905879

Best MySQL DigitalOcean Performance – ScaleGrid vs. DigitalOcean Managed Databases

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MySQL is the all-time number one open source database in the world, and a staple in RDBMS space. DigitalOcean is quickly building its reputation as the developers cloud by providing an affordable, flexible and easy to use cloud platform for developers to work with. MySQL on DigitalOcean is a natural fit, but what’s the best way to deploy your cloud database? In this post, we are going to compare the top two providers, DigitalOcean Managed Databases for MySQL vs. ScaleGrid MySQL hosting on DigitalOcean.

At a glance – TLDR
ScaleGrid Blog - At a glance overview - 1st pointCompare Throughput
ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads. Read now

ScaleGrid Blog - At a glance overview - 2nd pointCompare Latency
On average, ScaleGrid achieves almost 30% lower latency over DigitalOcean for the same deployment configurations. Read now

ScaleGrid Blog - At a glance overview - 3rd pointCompare Pricing
ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. Read now

MySQL DigitalOcean Performance Benchmark
In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. We are going to use a common, popular plan size using the below configurations for this performance benchmark:

Comparison Overview
ScaleGridDigitalOceanInstance TypeMedium: 4 vCPUsMedium: 4 vCPUsMySQL Version8.0.208.0.20RAM8GB8GBSSD140GB115GBDeployment TypeStandaloneStandaloneRegionSF03SF03SupportIncludedBusiness-level support included with account sizes over $500/monthMonthly Price$120$120

As you can see above, ScaleGrid and DigitalOcean offer the same plan configurations across this plan size, apart from SSD where ScaleGrid provides over 20% more storage for the same price.

To ensure the most accurate results in our performance tests, we run the benchmark four times for each comparison to find the average performance across throughput and latency over read-intensive workloads, balanced workloads, and write-intensive workloads.

Throughput
In this benchmark, we measure MySQL throughput in terms of queries per second (QPS) to measure our query efficiency. To quickly summarize the results, we display read-intensive, write-intensive and balanced workload averages below for 150 threads for ScaleGrid vs. DigitalOcean MySQL:

ScaleGrid MySQL vs DigitalOcean Managed Databases - Throughput Performance Graph

For the common 150 thread comparison, ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads.

#cloud #database #developer #digital ocean #mysql #performance #scalegrid #95th percentile latency #balanced workloads #developers cloud #digitalocean droplet #digitalocean managed databases #digitalocean performance #digitalocean pricing #higher throughput #latency benchmark #lower latency #mysql benchmark setup #mysql client threads #mysql configuration #mysql digitalocean #mysql latency #mysql on digitalocean #mysql throughput #performance benchmark #queries per second #read-intensive #scalegrid mysql #scalegrid vs. digitalocean #throughput benchmark #write-intensive

PWA vs Native App: Which Is Better Option In 2021?

Every year, the world is expanding with the launch of new smartphones and other gadgets available in the market. According to Statista, more than 50% of the population will be using smartphones by the end of 2021.

Hence, businesses worldwide have understood the importance of smartphones and are joining the mobile industry by launching native apps.

Apart from native apps, progressive web apps is another technology that is gaining a lot of attention among businesses. Moreover, various leading companies worldwide have openly accepted PWA and built progressive web apps.

Now, the question arises, how is PWA different from the native apps? Read More

#pwa vs native #pwa vs native app #progressive web app vs native #progressive web app vs native app #pwa vs native app performance