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license: cc-by-nd-4.0 |
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# Time-Series Donations Dataset |
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## Overview |
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This repository provides a time-series dataset of donation dynamics over time. |
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It is intended for experiments in: |
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- Time-series forecasting |
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- Trend and seasonality analysis |
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- Anomaly detection on donation flows |
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- Benchmarking classical and deep time-series models |
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The data are organized in a tabular time-series format, with each row representing a time step and each column representing a numerical or categorical feature related to donations. |
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## Repository Contents |
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- `time series adv-donations.xlsx` |
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Main time-series file containing the donation-related data. |
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(If additional files are later added—e.g., CSV exports or documentation—they can be listed here.) |
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## Data Schema (example) |
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Columns may include (adjust to your actual header names): |
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- `date` or `timestamp`: time index of the observation |
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- `donation_amount`: total donated amount in the given time window |
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- `donation_count`: number of donations in the given time window |
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- `campaign_id` or `channel`: optional categorical identifiers |
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- Other engineered or contextual features (e.g., weekday, holiday flags, etc.) |
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## How to Use |
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This dataset is suitable for: |
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- Building forecasting models (ARIMA, Prophet, LSTM, TCN, Transformers, etc.) |
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- Comparing different time-series pipelines |
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- Exploring seasonality, trends, and external influences on donation behavior |
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- Teaching and experimentation in time-series modeling |
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No personal or directly identifying donor information is included in this dataset. |
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## Loading the Dataset from Hugging Face |
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Below are example snippets showing how to download and load the dataset directly from Hugging Face. |
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### 1. Using `huggingface_hub` + `pandas` (recommended for Excel) |
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```python |
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from datasets import load_dataset |
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# Carica il dataset dal repo Hugging Face (sostituisci con il tuo repo_id) |
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ds = load_dataset("VillanovaAI/Time-Series-Donations") |
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# ds è un DatasetDict; si assume che il file CSV/Parquet generi lo split “train” |
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df = ds["train"].to_pandas() # converte in pandas DataFrame |
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print(df.head()) |
