Fixed typos for MTBench

Author: gaukhar-n

app/projects/mtbench/page.mdx

@@ -34,7 +34,7 @@ data was missing for more than 70% of the time period due to market closures (e.
 ### Weather Dataset
 We selected 50 airports in the United States as data sources, using the Global Historical Climatology Network Hourly (GHCN-H) dataset. The data spans from 2003 to 2020 and is collected hourly. Each weather station records multiple attributes, including geographical location, temperature, humidity, wind speed, wind direction, visibility, pressure, and precipitation. Airports were chosen due to the higher reliability and accuracy of their weather data compared to other stations. In this study, we focus on single-channel data, specifically temperature, as it is the most critical parameter for weather forecasting. Meanwhile, within our raw data creation pipeline, additional channels are available, allowing for future expansion to multi-channel weather analysis. 
 Unlike stock price datasets, systematically collecting weather-related news is challenging, as routine weather reports may not provide sufficient context for complex reasoning. To address this, we use the Storm Events Database, which documents storm occurrences in the United States from 1950 to 2020. This dataset includes details such as storm type, location, fatalities, and injuries, covering a range of severe weather conditions, such as hail, tornadoes, thunderstorms, floods, hurricanes, and typhoons. 
-Each entry contains an <em>event ID<em> and an <em>episode ID<em>, where the event ID uniquely identifies an occurrence, and the <em>episode ID<em> links related events. For example, a hurricane may trigger multiple tornadoes, hailstorms, and thunderstorms, all grouped under the same <em>episode ID<em>. Each event also includes a textual description, providing valuable contextual information.
+Each entry contains an <em>event ID</em> and an <em>episode ID</em>, where the event ID uniquely identifies an occurrence, and the <em>episode ID</em> links related events. For example, a hurricane may trigger multiple tornadoes, hailstorms, and thunderstorms, all grouped under the same <em>episode ID</em>. Each event also includes a textual description, providing valuable contextual information.
 
 ## Experiments