It's the annual sales event where the retailer slashes prices on thousands of products across its online storefront (and triggers similar sales on other retailers too). Prime Day, over time, has become two full days of shopping instead of one, and in 2022 there were two Prime Day events, one in the summer and one in the fall (because money). This year's Prime Day event falls on July 11 and 12, starting at 3 am Eastern time. You need to be a Prime subscriber to take advantage of the discounts (here's a free 30-day trial; set a reminder to cancel it before it automatically renews), but there are plenty of deals for anyone who isn't a member. WIRED will be scouring the platform to bring you the best deals throughout the event, but there's good news: Price drops have already started. We rounded up the best early Prime Day deals we've found right here--on Amazon and other retailers--and we'll keep this story updated. Our Prime Day Shopping Guide is full of tips to help you navigate the event like a pro.

Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated models typically rely on a large number of time series examples with substantial history. A rapidly growing topic of interest is forecasting time series which lack sufficient historical data -- often referred to as the ``cold start'' problem. In this paper, we introduce a novel yet simple method to address this problem by leveraging graph neural networks (GNNs) as a data augmentation for enhancing the encoder used by such forecasters. These GNN-based features can capture complex inter-series relationships, and their generation process can be optimized end-to-end with the forecasting task. We show that our architecture can use either data-driven or domain knowledge-defined graphs, scaling to incorporate information from multiple very large graphs with millions of nodes. In our target application of demand forecasting for a large e-commerce retailer, we demonstrate on both a small dataset of 100K products and a large dataset with over 2 million products that our method improves overall performance over competitive baseline models. More importantly, we show that it brings substantially more gains to ``cold start'' products such as those newly launched or recently out-of-stock.

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed frequency time series panel data structures. Our empirical results show the superior performance of our machine learning panel data regression models over analysts' predictions, forecast combinations, firm-specific time series regression models, and standard machine learning methods.

Multimodal demand forecasting aims at predicting product demand utilizing visual, textual, and contextual information. This paper proposes a method for multimodal product demand forecasting using convolutional, graph-based, and transformer-based architectures. Traditional approaches to demand forecasting rely on historical demand, product categories, and additional contextual information such as seasonality and events. However, these approaches have several shortcomings, such as the cold start problem making it difficult to predict product demand until sufficient historical data is available for a particular product, and their inability to properly deal with category dynamics. By incorporating multimodal information, such as product images and textual descriptions, our architecture aims to address the shortcomings of traditional approaches and outperform them. The experiments conducted on a large real-world dataset show that the proposed approach effectively predicts demand for a wide range of products. The multimodal pipeline presented in this work enhances the accuracy and reliability of the predictions, demonstrating the potential of leveraging multimodal information in product demand forecasting.

Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., ''regimes'' and ''components'', for which we present CubeScope, an efficient and effective method over high-order tensor streams. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, ''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden ''components'' representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed.

Amazon Prime Day 2023 is one week away on July 11th, but you don't have to wait until then to get a good deal. The company has started to roll out a few early Prime Day deals before the two-day shopping event officially commences, including, as expected, several discounts on its own devices and services. We've rounded up the best early access Prime Day deals we can find below. Remember that you'll need to subscribe to Prime to take advantage of many (but not all) of the offers, and that there's always a chance that prices drop lower during the event itself. For those with no interest in Prime, we've also included a few of the best tech deals from this week that aren't explicitly tied to the event. We'll stay on the lookout as Prime Day gets nearer and update this roundup with new offers as they arise. Amazon's largest tablet is good for streaming and browsing and right now Prime members can snag it for half price.

As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business. However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. Specifically, OpenSiteRec leverages a heterogeneous graph schema to represent various types of real-world entities and relations in four international metropolises. To evaluate the performance of the existing general methods on the site recommendation task, we conduct benchmarking experiments of several representative recommendation models on OpenSiteRec. Furthermore, we also highlight the potential application directions to demonstrate the wide applicability of OpenSiteRec. We believe that our OpenSiteRec dataset is significant and anticipated to encourage the development of advanced methods for site recommendation. OpenSiteRec is available online at https://OpenSiteRec.github.io/.

Amazon has announced that Prime Day 2023 will begin on July 11th, but you don't have to wait until then to get a good deal. The company has started to roll out a few early Prime Day deals before the two-day shopping event officially commences, including, as expected, several discounts on its own devices and services. We've rounded up the best early access Prime Day deals we can find below. Remember that you'll need to subscribe to Prime to take advantage of many (but not all) of the offers, and that there's always a chance that prices drop lower during the event itself. For those with no interest in Prime, we've also included a few of the best tech deals from this week that aren't explicitly tied to the event.

The United States Federal Trade Commission has accused Amazon.com of enrolling millions of consumers into its paid subscription Amazon Prime service without their consent and making it hard for them to cancel, the latest action by the agency against the e-commerce giant in recent weeks. The FTC sued in Amazon in federal court in Seattle on Wednesday, alleging that "Amazon has knowingly duped millions of consumers into unknowingly enrolling in Amazon Prime." The FTC said Amazon used "manipulative, coercive or deceptive user-interface designs known as'dark patterns' to trick consumers into enrolling in automatically renewing Prime subscriptions." The lawsuit is one of several actions taken by President Joe Biden's administration intended to rein in the outsized market power of Big Tech firms as it tries to increase competition to create greater consumer protection. The FTC said Amazon Prime is the world's largest subscription programme, generating $25bn in revenue annually.

Amazon has announced the dates for its next annual shopping event. Prime Day 2023 will be on July 11th and 12th this year, beginning at 12AM PT/ 3AM ET on Tuesday, July 11th, and concluding at the end of Wednesday, July 12th. Prime Day isn't necessarily a perk of Amazon's subscription service, like access to Prime Video content, but most deals on Amazon during the two-day event are exclusively available to Prime members. The cost of Prime has increased quite a bit since its launch in 2005, and even in the past few years. An annual membership will set you back $139 right now, $20 more than its previous price.