Predicting Socio-Economic Indicators using News Events

Chakraborty, Sunandan; Venkataraman, Ashwin; Jagabathula, Srikanth; Subramanian, Lakshminarayanan

doi:10.1145/2939672.2939817

Cited by 23 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social media and news play an important role in driving the fluctuation of economic indicators and financial markets [1], [2], [3], [4] in a nontrivial fashion. Recently, novel financial markets have emerged, that are exchanging from fiat money (USD, EUR, CNY) to cryptocurrencies and vice versa [5,6].…”

Section: Introductionmentioning

confidence: 99%

Sensing Social Media Signals for Cryptocurrency News

Beck

Huang

Lindner

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

View full text Add to dashboard Cite

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks. * Contributed equally to the first authorship † anino@ethz.ch

show abstract

Section: Introductionmentioning

confidence: 99%

Sensing Social Media Signals for Cryptocurrency News

Beck

Huang

Lindner

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

View full text Add to dashboard Cite

show abstract

“…This additional data could potentially increase the forecasting accuracy. Chakraborty et al (2016) have also already demonstrated that using news data improves forecast accuracy of produce prices. A generalized data retrieval framework would need to be implemented to scrape the numerous additional datasets live and combine them into vector-valued time series data.…”

Section: Improving Forecastsmentioning

confidence: 94%

An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors

Nowocin

Marathe

et al. 2019

Proceedings of the Tenth International Conference on Information and Communication Technologies and Development

View full text Add to dashboard Cite

Small and marginal farmers, who account for over 80% of India's agricultural population, often sell their harvest at low, unfavorable prices before spoilage. These farmers often lack access to either cold storage or market forecasts. In particular, by having access to cold storage, farmers can store their produce for longer and thus have more flexibility as to when they should sell their harvest by. Meanwhile, by having access to market forecasts, farmers can more easily identify which markets to sell at and when. While affordable cold storage solutions have become more widely available, there has been less work on produce price forecasting.A key challenge is that in many regions of India, predominantly in rural and remote areas, we have either very limited or no produce pricing data available from public online sources.In this paper, we present a produce price forecasting system that pulls data from the Indian Ministry of Agriculture and Farmers Welfare's website Agmarknet, trains a model of prices using over a thousand markets, and displays interpretable price forecasts in a web application viewable from a mobile phone. Due to the pricing data being extremely sparse, our method first imputes missing entries using collaborative filtering to obtain a dense dataset. Using this imputed dense dataset, we then train a decision-tree-based classifier to predict produce prices at different markets. In terms of interpretability, we display the most relevant historical pricing data that drive each forecasted price, where we take advantage of the fact that a wide family of decision-tree-based ensemble learning methods are adaptive nearest neighbor methods. We show how to construct heuristic price uncertainty intervals based on nearest neighbors. We validate forecast accuracy on data from Agmarknet and a small field survey of a few markets in Odisha. arXiv:1812.05173v1 [cs.CY] 12 Dec 2018 1 In India, a "marginal" farmer cultivates agrictultrual land up to 1 hectare, and a "small" farmer cultivates land from 1 to 2 hectares (Agriculture Census Division, 2014). An overview of challenges and opportunities for small farmers in India is provided by Mahendra Dev (2014).2

show abstract

“…Thus, another approach to predict agricultural commodity prices using unstructured data is from news articles. For example, Chakraborty et al [16] proposed a novel generative model of real-world events and employ it to extract events from a large corpus of news articles. The authors grouped events by event triggers, which are specific words that describe the events.…”

Section: Prediction Of Agriculture Commodity Price Using Unstructured Datamentioning

confidence: 99%

“…Accordingly, knowing the price of agricultural commodities in advance provides market participants (i.e., governments, farmers, consumers, and others) with advantages, such as providing a clearer understanding of the market and allowing the planning of business strategies and the adjustment of personal finances, among others. Thus, there have been many efforts to predict the future prices based on historical factors, such as earlier prices [11], product quality levels [12], climate change [13], seasonality factors [14], agricultural disasters [15], and other economic effects [16].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

et al. 2020

View full text Add to dashboard Cite

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

show abstract

Predicting Socio-Economic Indicators using News Events

Cited by 23 publications

References 30 publications

Sensing Social Media Signals for Cryptocurrency News

Sensing Social Media Signals for Cryptocurrency News

An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Contact Info

Product

Resources

About