Omkar2118/datasets huggingface dataset:
Extreme Events > Natural Disasters > Hurricane
Full name: Tourism > Finance > Sales Revenue

A new spatio-temporal benchmark dataset (Hurricane), is suited for forecasting during extreme events and anomalies. The dataset is provided through the Florida Department of Revenue which provides the monthly sales revenue (2003-2020) for the tourism industry for all 67 counties of Florida which are prone to annual hurricanes. Furthermore, we aligned and joined the raw time series with the history of hurricane categories (i.e., event intensities) based on time for each county.
Note that the hurricane category indicates the maximum sustained wind speed which can result in catastrophic damages as this number goes up (Category 1-6).

Introducing paper: AA-Forecast: Anomaly-Aware Forecast for Extreme Events
Consumer Spendings
Full name: Finance > US Economy > Consumer Spendings

State-level data for the US economy through the lens of consumer spending (Credit/Debit Spending) .  The dataset is enriched with state-level Economic Dynamics and Policy Responses. Specifically, we further enriched the data with the state-level policies as an indication of extreme events (e.g., the state’s business closure order). 

- **Date**: The date of the data record.
- **StateAbbr**: Abbreviation of the state.
- **Population**: Population of the state.
- **Spend_xxx**: Various spending metrics (e.g., `spend_acf`, `spend_aer`, `spend_all`). Spend_all (all categories)
- **Policy**: Indicator of policy measures.
- **lnDailyNewDeaths**, **DailyNewDeaths**: Logarithm and raw counts of daily new deaths.
- **lnDailyNewCases**, **DailyNewCases**: Logarithm and raw counts of daily new cases.
- **Deaths**, **Cases**: Cumulative counts.
- **Day**, **Day_**, **DayOfYear**: Day-related information.

Introducing paper: AA-Forecast: Anomaly-Aware Forecast for Extreme Events
Climabench

The topic of Climate Change (CC) has received limited attention in NLP despite its real world urgency. Activists and policy-makers need NLP tools in order to effectively process the vast and rapidly growing textual data produced on CC. Their utility, however, primarily depends on whether the current state-of-the-art models can generalize across various tasks in the CC domain. In order to address this gap, we introduce Climate Change Benchmark (ClimaBench), a benchmark collection of existing disparate datasets for evaluating model performance across a diverse set of CC NLU tasks systematically. Further, we enhance the benchmark by releasing two large-scale labelled text classification and question-answering datasets curated from publicly available environmental disclosures. Lastly, we provide an analysis of several generic and CC-oriented models answering whether fine-tuning on domain text offers any improvements across these tasks. We hope this work provides a standard assessment tool for research on CC text data.

Introducing paper: Towards Answering Climate Questionnaires from Unstructured Climate Reports