Depending on which one was used, a traveller from Toledo could end up anywhere between sixty miles outside Rome and more than six hundred miles away, on the plains of eastern Bulgaria. But the graph was meant to show just how divergent the estimates were. All the estimates were too large-we now know that the correct distance is sixteen and a half degrees. There was a cluster of estimates at around twenty degrees, including those made by the great astronomer Tycho Brahe and the pioneering cartographer Gerardus Mercator others, including the celebrated mathematician Ptolemy, put the distance between the two cities closer to thirty degrees. The estimates were wildly different, scattered all across the line. From this point, he drew a single horizontal line on the page, marking across its length twelve historical calculations of the longitudinal distance from Toledo to Rome. On the left, he drew a tick mark, representing the ancient city of Toledo, in Spain. To make his case, he drew a simple one-dimensional graph. ![]() In 1628, van Langren wrote a letter to the Spanish court, in an effort to demonstrate the importance of improving the way longitude was calculated (and of giving him the funding to do so). If establishing a north-south position was notoriously difficult, the spin of the Earth made it nearly impossible to accurately calculate a ship’s east-west position. Mariners had to rely on error-prone charts and faulty compasses they made celestial observations while standing on the decks of rocking boats, and-if all else failed-threw rope overboard in an attempt to work out how far from the seabed they were. Such measurements were particularly important at sea, where accurate navigation presented a considerable challenge. This was well into the Age of Discovery, and Europeans were concerned with the measurement of time, distance, and location. The book begins with what might be the first statistical graph in history, devised by the Dutch cartographer Michael Florent van Langren in the sixteen-twenties. In “ A History of Data Visualization and Graphic Communication” (Harvard), Michael Friendly and Howard Wainer, a psychologist and a statistician, argue that visual thinking, by revealing what would otherwise remain invisible, has had a profound effect on the way we approach problems. The right graph, he pointed out, would have shown the truth at a glance. A decade later, Edward Tufte, the great maven of data visualization, used the Challenger teleconference as a potent example of the wrong way to display quantitative evidence. Soon after takeoff, the rubber O-rings leaked, a joint in the solid rocket boosters failed, and the space shuttle broke apart, killing all seven crew members. This is why the managers made the tragic decision to go ahead despite the weather. The chart implicitly defined the scope of relevance-and nobody seems to have asked for additional data points, the ones they couldn’t see. But most of the experts were unconvinced. Some engineers used the chart to argue that the shuttle’s O-rings had malfunctioned in the cold before, and might again. As Diane Vaughn relates in her account of the tragedy, “ The Challenger Launch Decision” (1996), the data were presented at an emergency NASA teleconference, scribbled by hand in a simple table format and hurriedly faxed to the Kennedy Space Center. The first graph contains data compiled the evening before the disastrous launch of the space shuttle Challenger, in 1986. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.One more twist: the points on the graph are real but have nothing to do with auto racing. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). ![]() However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks.
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