投稿時間:2021-01-28 04:46:44 RSSフィード2021-01-28 04:00 分まとめ(67件)

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js	JavaScriptタグが付けられた新着投稿 - Qiita	React.memo / useCallback / useMemo を書きながら学ぶ	https://qiita.com/ryosuketter/items/c503d98e16c28f516c4d	useCallbackの注意点前述の通り、useCallbackはReactmemoと併用するものなので、次のような使い方をしても再レンダリングをスキップできませんReactmemoでメモ化していないコンポネントにuseCallbackでメモ化したコールバック関数を渡すときuseCallbackでメモ化したコールバック関数を、それを生成したコンポーネント自身で利用するときuseCallbackの疑問点依存関係の配列にsetState関数を含める必要があるかない。	2021-01-28 02:51:26
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Class全体を指すものが欲しい	https://teratail.com/questions/318957?rss=all	Class全体を指すものが欲しい前提・実現したいこと現在Cでライブラリを作っています。	2021-01-28 02:28:32
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Rails】今月を常に表示させるカレンダーの作り方	https://qiita.com/redrabbit1104/items/3a0d4adae86603009c70	日付からを引いた後に、その日付の曜日の値とxが一致した場合にのみ表示するようにします。	2021-01-28 02:14:29
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【rails】Gem「bullet」の具体的な効果	https://qiita.com/jus_37/items/098b673368454f73cbb6	他にも読み込みを追加で必要な場合やカウンターキャッシュを使用する場合も通知するらしいですが、今回はN問題をへらすことのみに着目しますGoogle翻訳がわかりやすい、、ということでアプリケーションを開発しているときに、無駄なクエリの読み込みしている場合に通知してくれるとか。	2021-01-28 02:00:50
Ruby	Railsタグが付けられた新着投稿 - Qiita	【rails】Gem「bullet」の具体的な効果	https://qiita.com/jus_37/items/098b673368454f73cbb6	他にも読み込みを追加で必要な場合やカウンターキャッシュを使用する場合も通知するらしいですが、今回はN問題をへらすことのみに着目しますGoogle翻訳がわかりやすい、、ということでアプリケーションを開発しているときに、無駄なクエリの読み込みしている場合に通知してくれるとか。	2021-01-28 02:00:50
Apple	AppleInsider - Frontpage News	This M1 MacBook Air with 16GB RAM, 512GB SSD is in stock and $100 off	https://appleinsider.com/articles/21/01/27/this-m1-macbook-air-with-16gb-ram-512gb-ssd-is-in-stock-and-100-off	This M MacBook Air with GB RAM GB SSD is in stock and offApple s MacBook Air with the new M chip GB of memory and a spacious GB SSD is discounted to and in stock for delivery to your door just in time for Valentine s Day MacBook Air M in stockThe discount applies to the Late MacBook Air equipped with Apple s M chip with a core GPU GB of RAM and a GB SSD in gold when shopping at Apple Authorized Reseller Adorama with coupon code APINSIDER At press time units are in stock and ready to ship with free delivery within the contiguous U S In comparison Apple itself has this spec on backorder at MSRP for nearly a month due to high demand Read more	2021-01-27 17:41:55
Apple	AppleInsider - Frontpage News	Apple AirPods are still the top product in expanding wireless headphone market	https://appleinsider.com/articles/21/01/27/apple-airpods-are-still-the-top-product-in-expanding-wireless-headphone-market	Apple AirPods are still the top product in expanding wireless headphone marketApple s AirPods continued to dominate the wireless headphone market in holding a nearly share as the segment saw tremendous growth Credit Andrew O Hara AppleInsiderAccording to new data from Strategy Analytics the total number of Bluetooth headphones shipped in is estimated to have pushed beyond million That represents total market growth of nearly year over year Read more	2021-01-27 17:22:35
海外TECH	Engadget	EA founded a new studio to work on the long-awaited 'Skate 4'	https://www.engadget.com/ea-full-circle-skate-4-studio-175608905.html	EA founded a new studio to work on the long awaited x Skate x EA has formed a new studio to develop the next Skate game On Wednesday the publisher announced the existence of Full Circle which like previous Skate developer Black Box will be based out of Vancouver Canada EA has tapped Daniel McCulloch who	2021-01-27 17:56:08
海外TECH	Engadget	'Control: Ultimate Edition' headlines February's batch of PS Plus freebies	https://www.engadget.com/ps-plus-free-games-february-control-destruction-allstars-concrete-genie-174552675.html	x Control Ultimate Edition x headlines February x s batch of PS Plus freebiesSony has revealed the next batch of free games for PlayStation Plus subscribers ーControl Ultimate Edition Destruction AllStars PS only and Concrete Genie ーwhich you can claim from February nd The inclusion of Control has caused some outcry g	2021-01-27 17:45:52
海外TECH	Engadget	EVs with Google Maps will make it easier to plan trips around recharging	https://www.engadget.com/google-maps-electric-vehicles-route-planning-charging-stations-170037213.html	EVs with Google Maps will make it easier to plan trips around rechargingCars with Google Maps built in are getting a few more features that should make it easier to plan trips around charging stops Google is using graph theory in its latest routing algorithms to help you determine the best way to get to your destination	2021-01-27 17:00:37
海外科学	NYT > Science	Restoring Environmental Rules Rolled Back by Trump Could Take Years	https://www.nytimes.com/2021/01/22/climate/biden-environment.html	Restoring Environmental Rules Rolled Back by Trump Could Take YearsPresident Biden has promised to reinstate more than rules and regulations aimed at environmental protection that his predecessor rolled back It won t happen overnight	2021-01-27 17:46:48
海外TECH	WIRED	Cops Disrupt Emotet, the Internet's ‘Most Dangerous Malware’	https://www.wired.com/story/emotet-botnet-takedown	cybercriminals	2021-01-27 17:46:17
海外科学	BBC News - Science & Environment	Legal threat over bee-harming pesticide use	https://www.bbc.co.uk/news/science-environment-55766035	harms	2021-01-27 17:03:14
ニュース	@日本経済新聞 電子版	透明な人事、やりがい生む　「省庁横断の幹部公募制を」 https://t.co/1CzLtrzCXQ	https://twitter.com/nikkei/statuses/1354475473879724032	透明	2021-01-27 17:04:52
ニュース	@日本経済新聞 電子版	装置好調の東エレク、気になる車載半導体不足の行方 https://t.co/hara9unRvS	https://twitter.com/nikkei/statuses/1354475472885665793	車載	2021-01-27 17:04:52
ニュース	@日本経済新聞 電子版	新型コロナや脱炭素で協力　日米首脳が初の電話協議　 https://t.co/VczufEdErl	https://twitter.com/nikkei/statuses/1354475471707115527	首脳	2021-01-27 17:04:52
海外ニュース	Japan Times latest articles	A week into Biden’s term, signs point to continued U.S.-China friction	https://www.japantimes.co.jp/news/2021/01/27/asia-pacific/us-china-policy-biden/	A week into Biden s term signs point to continued U S China frictionA flurry of military activity around Taiwan has highlighted ongoing strains in the relationship even as both sides warn against confrontation	2021-01-28 03:30:22
海外ニュース	Japan Times latest articles	Third time’s a charm? Japan gets another high-level U.S. commitment to defending Senkakus	https://www.japantimes.co.jp/news/2021/01/27/national/blinken-senkakus-defense-commitment/	Third time s a charm Japan gets another high level U S commitment to defending SenkakusNew U S Secretary of State Antony Blinken was the third high level U S official in a week to reaffirm that the islets fall under the U S Japan	2021-01-28 02:20:33
海外ニュース	Japan Times latest articles	Eating out in Japan: If you must do it, turn down the volume	https://www.japantimes.co.jp/news/2021/01/27/national/mokushoku-japan-restaurants-coronavirus/	fukuoka	2021-01-28 02:03:46
海外ニュース	Japan Times latest articles	Mima Ito aiming for gold with or without fans at Tokyo Games	https://www.japantimes.co.jp/sports/2021/01/27/olympics/summer-olympics/olympics-table-tennis/mima-ito-tokyo-olympics/	olympics	2021-01-28 03:26:12
海外ニュース	Japan Times latest articles	Fake news becoming real issue for foreign sumo fans	https://www.japantimes.co.jp/sports/2021/01/27/sumo/fake-news-foreign-sumo-fans/	mainstream	2021-01-28 03:25:11
海外ニュース	Japan Times latest articles	Cricket Australia confirms Indian players were racially abused during test	https://www.japantimes.co.jp/sports/2021/01/27/more-sports/cricket-2/cricket-australia-indian-players/	Cricket Australia confirms Indian players were racially abused during testA Cricket Australia probe Wednesday concluded Indian players were racially abused during the third test at the Sydney Cricket Ground but cleared six people who	2021-01-28 02:57:51
海外ニュース	Japan Times latest articles	IOC urges athletes to get vaccinated before Tokyo Games	https://www.japantimes.co.jp/sports/2021/01/27/olympics/summer-olympics/ioc-athletes-vaccines-tokyo-olympics/	coronavirus	2021-01-28 02:08:05
海外ニュース	Japan Times latest articles	Biden gets dream team on China: But what if the game is changing?	https://www.japantimes.co.jp/opinion/2021/01/27/commentary/world-commentary/biden-dream-team-china/	Biden gets dream team on China But what if the game is changing It is unclear whether Biden s team can remain immune to the wish lists of the consulting firms and corporate hands that put a silver lining on	2021-01-28 02:30:52
ニュース	BBC News - Home	Covid-19: England's schools will not reopen before March	https://www.bbc.co.uk/news/uk-55828952	covid	2021-01-27 17:47:35
ニュース	BBC News - Home	Coronavirus: EU demands UK-made AstraZeneca vaccine doses	https://www.bbc.co.uk/news/world-europe-55822602	astrazeneca	2021-01-27 17:52:06
ニュース	BBC News - Home	Covid: Would-be travellers must prove journey is essential - Patel	https://www.bbc.co.uk/news/uk-55821702	covid	2021-01-27 17:53:23
ニュース	BBC News - Home	Covid-19: England's schools closed until March, and EU demands vaccine from UK plants	https://www.bbc.co.uk/news/uk-55823857	coronavirus	2021-01-27 17:43:07
ニュース	BBC News - Home	Covid: Wrexham vaccine production resumes after suspect package	https://www.bbc.co.uk/news/uk-wales-55822838	astrazeneca	2021-01-27 17:23:36
ニュース	BBC News - Home	Covid-19 in the UK: How many coronavirus cases are there in your area?	https://www.bbc.co.uk/news/uk-51768274	cases	2021-01-27 17:36:23
ニュース	BBC News - Home	Covid: What’s happening to the EU vaccine scheme?	https://www.bbc.co.uk/news/explainers-52380823	coronavirus	2021-01-27 17:04:24
ビジネス	ダイヤモンド・オンライン - 新着記事	「お前はダメ！　PDCAがなってない！」と上司から怒られたときの最強の対応策 - 独学大全	https://diamond.jp/articles/-/255002	読書	2021-01-28 02:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	単なるもの忘れはセーフ？ 揺れる「認知症」診断基準 - 脳の毒を出す食事	https://diamond.jp/articles/-/260162	単なるもの忘れはセーフ揺れる「認知症」診断基準脳の毒を出す食事現代人の脳には“毒が溜まっている無意識に溜まった脳の“毒を出して脳がみるみる若返る食事法を紹介する脳の若返りと認知症治療の専門医・白澤卓二医師が提案する衝撃の最新刊『脳の毒を出す食事』では、現代人の脳に溜まった毒を出し、脳の機能を上げる食事法を紹介している。	2021-01-28 02:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「次々いい考えを出せる人」と「いつも沈黙の人」の決定的な差 - 考える術	https://diamond.jp/articles/-/258888	「次々いい考えを出せる人」と「いつも沈黙の人」の決定的な差考える術鬼才が「考える」ためのあらゆる方法を大公開「何も浮かばない」とうんうんうなるより、この本をパッと開いてそのワザを使ってみてください。	2021-01-28 02:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	なぜ、社員のやりがいを追求すると 会社の業績はどんどん上がるのか？ - ワークマン式「しない経営」	https://diamond.jp/articles/-/259016		2021-01-28 02:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	壁に手を添えるだけで効く！「スゴレッチ」 リモート腰痛・肩こり・首こり解消ストレッチ - スゴレッチ	https://diamond.jp/articles/-/260705	解消	2021-01-28 02:35:00
GCP	Cloud Blog	Retailers find flexible demand forecasting models in BigQuery ML	https://cloud.google.com/blog/products/data-analytics/get-started-with-data-analytics-demand-forecasting-with-ml-models/	Retailers find flexible demand forecasting models in BigQuery MLRetail businesses understand the value of demand forecastingーusing their intuition product and market experience and seasonal patterns and cycles to plan for future demand Beyond the need for forecasts that are as accurate as possible modern retailers also face the challenge of being able to perform demand planning at scale Product assortments that span tens of thousands of items across hundreds of individual selling locations or designated marketing areas lead to a number of time series that cannot be managed without the help of big data platforms and time series modeling solutions that scale accordingly  So far there have been two ways to address this challenge  Purchase a full end to end demand forecasting solution which takes significant time and resources to implement and maintain  Or leverage an all purpose machine learning platform to run your own time series models which requires deep experience in both modeling and data engineering  To help retailers with an easier more flexible solution for demand planning we ve published a Smart Analytics reference pattern for performing time series forecasting with BigQuery ML using autoregressive integrated moving average ARIMA as a basis This ARIMA model follows the BigQuery ML low code design principle allowing for accurate forecasts without advanced knowledge of time series models Moreover the BigQuery ML ARIMA model provides several innovations over the original ARIMA models that many are familiar with including the ability to capture multiple seasonal patterns automated model selection a no hassle preprocessing pipeline and most of all the ability to effortlessly generate thousands of forecasts at scale with nothing but a few lines of SQL In this blog we ll take a look at the two most common ways demand forecasting teams have been organized and how BigQuery ML fills a gap between the two plus discuss how BigQuery ML can help your demand planning recover from unforeseen events like COVID  To see the end to end process to implement the demand forecasting design pattern check out this video Two types of demand forecasting teamsHistorically large organizations have had two types of demand forecasting teams We ll call them the Business Forecasting team and the Science Forecasting team  The Business Forecasting team typically uses full enterprise resource planning ERP or software as a service SaaS forecasting solutions or occasionally a homegrown solution that don t require an advanced level of data science skill to use These ERPs produce entirely automated forecasts Team members often come from the business side of the organization and instead of deep technical skills bring extensive domain and business knowledge to their role Many large brick and mortar organizations often use this approach These types of solutions may scale well but they require significant time and resources both to implement and to support This typically includes large implementation and DevOps teams multiple dedicated compute and data storage instances and fixed schedule hours long batch cycles to refresh the forecasts The Science Forecasting team typically features PhD or MSc level practitioners working within a data science or a tech organization who are fluent in Python or R They work with a Cloud AI platform and perform all of the end to end forecasting themselves choosing building training and evaluating a model Then they deploy the model to production and communicate results to business stakeholders and leadership This type of team is often found in digital native organizations A new type of forecasting teamRecently a new hybrid type of forecasting team has emerged Often these are in businesses looking to become more data and model driven but don t have the resources to invest in an expensive ERP or hire a PhD level data scientist They may have a decent knowledge of forecasting and demand planning but not enough experience or organizational resources to deploy custom models at scale Still this type of team given the right tools has the potential to merge the best of both worlds the advanced modeling of the Science Forecaster and the deep domain knowledge of Business Forecaster Responding to the unforeseen As nearly every business experienced firsthand in certain events like the COVID pandemic throw a wrench into demand forecasting signals making existing models questionable With an ERP forecasting solution even a small change to the supply chain and store network configuration will result in a change in demand patterns that requires extensive reconfiguration of the demand planning solution and the help of a large support team BigQuery ML reduces the complexity of making such adjustments due to both expected and unexpected events and because it s serverless it autoscales and saves costs in DevOps time and efforts Regenerating forecasts to adapt to a change in the supply chain network configuration is now a matter of hours not weeks  Getting started with a BigQuery ML reference patternTo make it easier to get up and running with Google Cloud tools like BigQuery ML we recently introduced Smart Analytics reference patternsーtechnical reference guides with sample code for common analytics use cases We ve heard that you want easy ways to put analytics tools into practice and previous reference patterns cover use cases like predicting customer lifetime value propensity to purchase product recommendation systems and more  Our newest reference pattern on Github will help you get a head start on generating time series forecasts at scale The pattern will show you how to use historical sales data to train a demand forecasting model using BigQuery ML and then visualize the forecasts in a dashboard  For more details and to walk you through this process using historical transactional data for Iowa liquor sales data to forecast the next days check out our technical explainer In the blog you ll learn how to Pre process data into the correct format needed to create a demand forecasting model using BigQuery MLFit multiple BQ ARIMA time series models in BigQuery MLEvaluate the models and generate forward looking forecasts for the desired forecast horizonCreate a dashboard to visualize the projected demand using Data StudioSet up scheduled queries to automatically re fit the models on a regular basisClick to enlargeLet s do a deeper dive into the concepts we just introduced you to BigQuery ML bridges the gap between the Businesses Forecaster and the Science ForecasterGiven the features we just described we see how BQML helps fill the gap between the two current approaches to forecasting at scale allowing you to build your own demand forecasting platform without the need for highly specialized time series data scientists It s an ideal solution for hybrid forecasters featuring tools you can use to generate forecasts at scale on the fly  Since BigQuery ML lets you train and deploy ML models using SQL it democratizes your data modeling challenges opening up your demand forecasting tools and business insights to a larger pool of your organizational talent  For example the BigQuery ML ARIMA model helps retailers recover from unexpected events with the ability to generate thousands of forecasts with fresh data over a shorter amount of time You can recalibrate demand forecasts more cost effectively detect changes in trends and perform multiple iterations that capture new patterns as they emerge without mobilizing an entire DevOps team in order to do so  Using BigQuery ML as your forecast engine allows you to bridge the gap between your business or hybrid forecasting teams and advanced data science teams For example your forecast analysts will own the task of generating baseline statistical forecasts with BigQuery and reviewing them but they will loop in a senior data scientist to perform a more advanced causal impact analysis on some of their demand data as needed or to measure the effect of COVID on shifting demand patterns Think of it as “DemandOps instead of “DevOps  This is also possible if you already have ERP demand planning tools as well by simply exporting your forecasts and sales actuals into BigQuery whenever they are refreshed or as needed Chances are a retail organization actually has multiple time series forecasts being run by separate business functions Your merchandising team will be running tactical and operational demand forecasts finance is performing top line revenue forecasts while supply chain are running their own forecasts for capacity planning at the data center level each using their own specific tool set These forecasts are being generated in isolation but reconciling them would improve accuracy and provide the organization with valuable holistic insights into their business that siloed forecasts and analysis can t provide  For example based on market and product signals merchandising may forecast an increase in demand for a certain product Separately supply chain will be aware of various manufacturing and logistics stressors that project a decrease in the product shipments Typically this discrepancy won t be caught for several weeks and will then be resolved via emails and meetings By then it s too late since conflicting planning decisions were already made by the separate teams and the proverbial damage is done Using BigQuery as a centralized forecast analysis platform would allow a retailer to detect such discrepancy in a matter of hours or days and react accordingly instead of having to roll back planning decisions several weeks after the fact  BigQuery and BigQuery ML provide the perfect platform for collaboration between disparate and diverse forecasting teams beyond just the powerful modeling capabilities of BQARIMA  Google Cloud offers several solutions to help you enhance your demand forecasting capabilities and optimize inventory levels amidst changing times Besides the BigQuery ML tools described in this blog there are also  Building your own time series models either statistical or ML based using your preferred open source frameworks on Cloud AI Platform Jupyterlab instancesUse AutoML Forecast to automatically select and train cutting edge deep learning time series models Use our upcoming fully managed forecasting solution Demand AI currently in experimental status Work with a partner like o to implement their retail planning platform with forecasting capabilities on Google CloudFor more examples of data analytics reference patterns check out the predictive forecasting section in our catalog Ready to get started with BigQuery ML Read more in our product introduction Want to dig deeper into BigQuery ML capabilities Sign up here for free training on how to train evaluate and forecast inventory demand on retail sales data with BigQuery ML Related ArticleMost popular public datasets to enrich your BigQuery analysesCheck out free public datasets from Google Cloud available to help you get started easily with big data analytics in BigQuery and Cloud Read Article	2021-01-27 18:00:00
AWS	AWS Machine Learning Blog	Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA	https://aws.amazon.com/blogs/machine-learning/deepset-achieves-a-3-9x-speedup-and-12-8x-cost-reduction-for-training-nlp-models-by-working-with-aws-and-nvidia/	Deepset achieves a x speedup and x cost reduction for training NLP models by working with AWS and NVIDIAThis is a guest post from deepset creators of the open source frameworks FARM and Haystack and was contributed to by authors from NVIDIA and AWS nbsp At deepset we re building the next level search engine for business documents Our core product Haystack is an open source framework that enables developers to utilize the latest NLP models for …	2021-01-27 18:11:33
python	Pythonタグが付けられた新着投稿 - Qiita	Python Scrapyを使用したクローリング・スクレイピングプログラムの開発（準備編）	https://qiita.com/ezotaka/items/f32f8fca8526bd137253	コミット時にgitignoreで定義したファイルahreffnrelfootnotetitle具体的にはideaworkspacexmlとvenvディレクトリ以下のすべてのファイル が含まれていないことを確認しておきます。	2021-01-28 03:09:39
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	javascriptでrequest headerに任意の値を含めて画面遷移する方法を知りたい	https://teratail.com/questions/318962?rss=all	javascriptでrequestheaderに任意の値を含めて画面遷移する方法を知りたいrailsvueaxiosでToken認証を試していて、ログイン認証後画面遷移を行いたいのですが、遷移したいurlがvuerouterで定義したものではなくrailsのroutsrbで定義しているものに遷移したいです。	2021-01-28 03:30:31
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	パスが通らず困っています	https://teratail.com/questions/318961?rss=all	パスが通らず困っています前提・実現したいことパスを通したい。	2021-01-28 03:20:54
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	ESP32でHIDデバイスを制作したところproductNameの定義ができない	https://teratail.com/questions/318960?rss=all	デバッグにはこちらを参考にさせていただき、WebHIDnbspAPIを使用しています。	2021-01-28 03:20:12
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Excelにて連続する数値の省略表現を行いたい	https://teratail.com/questions/318959?rss=all	Excelにて連続する数値の省略表現を行いたいExcelにて、連続する数値の省略表現を行いたいです。	2021-01-28 03:00:40
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Java:repaint()について	https://teratail.com/questions/318958?rss=all	javarepaint	2021-01-28 03:00:34
海外TECH	Ars Technica	AT&T eats a $15.5 billion impairment charge as DirecTV debacle continues	https://arstechnica.com/?p=1737684	calendar	2021-01-27 18:26:10
海外TECH	Engadget	HBO Max didn't expect to have this many subscribers for another two years	https://www.engadget.com/hbo-max-didnt-expect-to-have-this-many-subscribers-for-another-two-years-182503219.html	HBO Max didn x t expect to have this many subscribers for another two yearsDuring its Q earnings call on Tuesday AT amp amp T revealed that its streaming subsidiary HBO Max now boasts some million paid monthly subscribers ーa figure neither company anticipated the service would attain until at least HBO overall	2021-01-27 18:25:03
海外TECH	CodeProject Latest Articles	Ionic Capacitor Angular Video Mobile App with SwipeClouds & Capacitor Plugins	https://www.codeproject.com/Articles/1175045/Ionic-Capacitor-Angular-Video-Mobile-App-with-Swip	Ionic Capacitor Angular Video Mobile App with SwipeClouds amp Capacitor PluginsIonic Capacitor Angular Mobile App with SwipeClouds for Playing Videos and A Custom Capacitor Plugin for Collecting User Data	2021-01-27 18:57:00
海外科学	NYT > Science	How Biden’s Climate Ambitions Could Shift America’s Global Footprint	https://www.nytimes.com/2021/01/27/climate/climate-change-biden-kerry.html	defense	2021-01-27 18:57:03
海外ニュース	Japan Times latest articles	Tokyo Olympics member says games going ahead ‘is up to the U.S.’	https://www.japantimes.co.jp/news/2021/01/27/national/olympics-tokyo-2020-member-games-u-s/	Tokyo Olympics member says games going ahead is up to the U S The International Olympic Committee and Japanese organizers have been increasingly bullish in recent weeks about the prospect of holding the postponed games	2021-01-28 04:54:58
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GCP	Cloud Blog	How to build demand forecasting models with BigQuery ML	https://cloud.google.com/blog/topics/developers-practitioners/how-build-demand-forecasting-models-bigquery-ml/	How to build demand forecasting models with BigQuery MLRetail businesses have a goldilocks problem when it comes to inventory don t stock too much but don t stock too little With potentially millions of products for a data science and engineering team to create multi millions of forecasts is one thing but to procure and manage the infrastructure to handle continuous model training and forecasting this can quickly become overwhelming especially for large businesses With BigQuery ML you can train and deploy machine learning models using SQL With the fully managed scalable infrastructure of BigQuery this means reducing complexity while accelerating time to production so you can spend more time using the forecasts to improve your business So how can you build demand forecasting models at scale with BigQuery ML for thousands to millions of products like for this liquor product below In this blogpost I ll show you how to build a time series model to forecast the demand of multiple products using BigQuery ML Using Iowa Liquor Sales data I ll use months of historical transactional data to forecast the next days You ll learn how to pre process data into the correct format needed to create a demand forecasting model using BigQuery MLtrain an ARIMA based time series model in BigQuery MLevaluate the modelpredict the future demand of each product over the next n daystake action on the forecasted predictions create a dashboard to visualize the forecasted demand using Data Studiosetup scheduled queries to automatically re train the model on a regular basisThe data Iowa Liquor SalesThe Iowa Liquor Sales data which is hosted publicly on BigQuery is a dataset that contains the spirits purchase information of Iowa Class “E liquor licensees by product and date of purchase from January to current from the official documentation by the State of Iowa The raw dataset looks like this As on any given date there may be multiple orders of the same product we need to Calculate the total of products sold grouped by the date and the productCleaned training dataIn the cleaned training data we now have one row per date per item name the total amount sold on that day This can be stored as a table or view In this example this is stored as bqmlforecast training data using CREATE TABLE Train the time series model using BigQuery MLTraining the time series model is straight forward  How does time series modeling work in BigQuery ML When you train a time series model with BigQuery ML multiple models components are used in the model creation pipeline ARIMA is one of the core algorithms Other components are also used as listed roughly in the order the steps they are run Pre processing Automatic cleaning adjustments to the input time series including missing values duplicated timestamps spike anomalies and accounting for abrupt level changes in the time series history Holiday effects Time series modeling in BigQuery ML can also account for holiday effects By default holiday effects modeling is disabled But since this data is from the United States and the data includes a minimum one year of daily data you can also specify an optional HOLIDAY REGION With holiday effects enabled spike and dip anomalies that appear during holidays will no longer be treated as anomalies A full list of the holiday regions can be found in the HOLIDAY REGION documentation Seasonal and trend decomposition using the Seasonal and Trend decomposition using Loess STL algorithm Seasonality extrapolation using the double exponential smoothing ETS algorithm Trend modeling using the ARIMA model and the auto ARIMA algorithm for automatic hyper parameter tuning In auto ARIMA dozens of candidate models are trained and evaluated in parallel which include p d q and drift The best model comes with the lowest Akaike information criterion AIC Forecasting multiple products in parallel with BigQuery MLYou can train a time series model to forecast a single product or forecast multiple products at the same time which is really convenient if you have thousands or millions of products to forecast To forecast multiple products at the same time different pipelines are run in parallel  In this example since you are training the model on multiple products in a single model creation statement you will need to specify the parameter TIME SERIES ID COL as item name Note that if you were only forecasting a single item then you would not need to specify TIME SERIES ID COL For more information see the BigQuery ML time series model creation documentation Evaluate the time series modelYou can use the ML EVALUATE function documentation to see the evaluation metrics of all the created models one per item As you can see in this example there were five models trained one for each of the products in item name The first four columns non seasonal p d q and has drift define the ARIMA model The next three metrics log likelihood AIC and variance are relevant to the ARIMA model fitting process The fitting process determines the best ARIMA model by using the auto ARIMA algorithm one for each time series Of these metrics AIC is typically the go to metric to evaluate how well a time series model fits the data while penalizing overly complex models As a rule of thumb the lower the AIC score the better Finally the seasonal periods detected for each of the five items happened to be the same WEEKLY Make predictions using the modelMake predictions using ML FORECAST syntax documentation which forecasts the next n values as set in horizon You can also change the confidence level the percentage that the forecasted values fall within the prediction interval The code below shows a forecast horizon of which means to make predictions on the next days since the training data was daily Since the horizon was set to the result contains rows equal to forecasted value number of items Each forecasted value also shows the upper and lower bound of the prediction interval given the confidence level As you may notice the SQL script uses DECLARE and EXECUTE IMMEDIATE to help parameterize the inputs for horizon and confidence level As these HORIZON and CONFIDENCE LEVEL variables make it easier to adjust the values later this can improve code readability and maintainability To learn about how this syntax works you can read the documentation on scripting in Standard SQL Plot the forecasted predictions You can use your favourite data visualization tool or use some template code here on Github for matplotlib and Data Studio as shown below How do you automatically re train the model on a regular basis If you re like many retail businesses that need to create fresh time series forecasts based on the most recent data you can use scheduled queries to automatically re run your SQL queries which includes your CREATE MODEL ML EVALUATE or ML FORECAST queries Create a new scheduled query in the BigQuery UIYou may need to first Enable Scheduled Queries before you can create your first one Input your requirements e g repeats Weekly and select Schedule Monitor your scheduled queries on the BigQuery Scheduled Queries pageExtra tips on using time series with BigQuery MLInspect the ARIMA model coefficientsIf you want to know the exact coefficients for each of your ARIMA models you can inspect them using ML ARIMA COEFFICIENTS documentation For each of the models ar coefficients shows the model coefficients of the autoregressive AR part of the ARIMA model Similarly ma coefficients shows the model coefficients of moving average MA part They are both arrays whose lengths are equal to non seasonal p and non seasonal q respectively The intercept or drift is the constant term in the ARIMA model SummaryCongratulations You now know how to train your time series models using BigQuery ML evaluate your model and use the results in production  Code on GithubYou can find the full code in this Jupyter notebook on Github Join me on February for a live walkthrough of how to train evaluate and forecast inventory demand on retail sales data with BigQuery ML I ll also demonstrate how to schedule model retraining on a regular basis so your forecast models can stay up to date You ll have a chance to have their questions answered by Google Cloud experts via chat Want more I m Polong Lin a Developer Advocate for Google Cloud Follow me on polonglin or connect with me on Linkedin at linkedin com in polonglin Please leave me your comments with any suggestions or feedback Thanks to reviewers Abhishek Kashyap Karl WeinmeisterRelated ArticleRetailers find flexible demand forecasting models in BigQuery MLTry BigQuery s design pattern for demand forecasting to create predictive analytics models for retail use cases Read Article	2021-01-27 19:30:00