Update README.md

README.md

@@ -48,33 +48,107 @@ language:
 tags:
 - wireless
 ---
+# 📶 Beam-Level (5G) Time-Series Dataset
 
-## Beam-Level (5G) Time-Series Dataset
-This dataset presents a novel, multi-variate time series specifically designed for advancing research in spatio-temporal forecasting. Our primary goal is to facilitate the accurate prediction of traffic throughput volumes across communication networks, as visually depicted in Figure 1.
+This dataset introduces a **novel multivariate time series** specifically curated to support research in **multivariate time series**. Its primary objective is to enable **accurate prediction of KPIs** across communication networks, as illustrated below:
 
-![An illustration of a base station (center), two cells (left and right), and four beams in each cell.](images/network.png)
+<p align="center">
+  <img src="images/network.png" alt="Base station, cells, and beams" />
+</p>
 
-The precise forecasting of network traffic volume is crucial for optimizing network flow management and efficient resource allocation. Consequently, this task holds significant practical and theoretical relevance for the scientific community in both networking and machine learning domains. The dataset aims to provide a valuable benchmark for researchers exploring state-of-the-art (SOTA) techniques in time series analysis.
+Precise forecasting of network traffic is critical for:
+- Optimizing **network management**
+- Enhancing **resource allocation efficiency**
 
+This task is of both **practical and theoretical importance** to researchers in networking and machine learning, offering a strong benchmark for state-of-the-art (SOTA) time series models.
 
-## Dataset Split
-This repository contains four datasets containing network performance metrics for 2,880 beams across 30 base stations. Each base station consists of 3 cells with 32 beams, with data recorded hourly. These datasets encompass a five-week period with data recorded at hourly intervals (as illustrated in Figure 2). These datasets are traffic_DLThpVol.csv, traffic_DLThpTime.csv, traffic_MR_number.csv, and traffic_DLPRB.csv. We remind the participants that the objective is to forecast future values of traffic volume (DLThpVol).
+---
+
+## 📂 Dataset Overview
+
+- **Beams:** 2,880  
+- **Base Stations:** 30  
+- **Cells per Station:** 3  
+- **Beams per Cell:** 32  
+- **Frequency:** Hourly  
+- **Duration:** 5 weeks + 2 target weeks  
+- **Total Hours (per beam):** Up to 840 (train) and 1176 (total)
+
+---
+
+## 📁 Available CSV Files
+
+### 🏋️‍♂️ Training Set (Weeks 0–5)
+
+| File Name                     | Metric                          |
+|------------------------------|----------------------------------|
+| `DLThpVol_train_0w-5w.csv`   | Downlink throughput volume       |
+| `DLThpTime_train_0w-5w.csv`  | Throughput transmission time     |
+| `DLPRB_train_0w-5w.csv`      | PRB (Physical Resource Block) usage |
+| `MR_number_train_0w-5w.csv`  | User count (Measurement Reports) |
+
+### 🎯 Forecast Targets
+
+#### 📆 6th Week (Week 5–6)
+
+| File Name                      | Metric                          |
+|--------------------------------|----------------------------------|
+| `DLThpVol_test_5w-6w.csv`      | Downlink throughput volume       |
+| `DLThpTime_test_5w-6w.csv`     | Throughput transmission time     |
+| `DLPRB_test_5w-6w.csv`         | PRB usage                        |
+| `MR_number_test_5w-6w.csv`     | User count                       |
+
+#### 📆 11th Week (Week 10–11)
+
+| File Name                        | Metric                          |
+|----------------------------------|----------------------------------|
+| `DLThpVol_test_10w-11w.csv`      | Downlink throughput volume       |
+| `DLThpTime_test_10w-11w.csv`     | Throughput transmission time     |
+| `DLPRB_test_10w-11w.csv`         | PRB usage                        |
+| `MR_number_test_10w-11w.csv`     | User count                       |
 
-![Dataset splits: train set (first 5 weeks), and two target weeks (immediate 6th week and the 11th week).](images/dataset_split.png)
 
-####  Dataset splits: train set (first 5 weeks), and two target weeks (immediate 6th week and the 11th week).
+---
+
+## 🧪 Dataset Splits
+
+<p align="center">
+  <img src="images/dataset_split.png" alt="Dataset train/forecast split" />
+</p>
+
+- **Training Set:** First 5 weeks  
+- **Forecast Targets:**  
+  - **Week 6** (immediate future)  
+  - **Week 11** (long-term future)
+
+---
+
+## 📄 Data Format
 
+Each CSV file follows this structure:
 
-Each dataset corresponds to a specific network performance metric:
+- **`Time` column**:  
+  - Ranges:
+    - `0–839` for training (weeks 1–6)  
+    - `0–167` for week 6  
+    - `168–335` for week 11  
 
-- traffic_DLThpVol.csv: represents throughput volume.
-- traffic_DLThpTime.csv: represents throughput time.
-- traffic_ DLPRB.csv: represents Physical Resource Block (PRB) utilization.
-- traffic_MR_number.csv: represents user count.
+- **Beam columns (`0_0_0`, ..., `29_2_31`)**:  
+  - Each uniquely identifies one of the **2,880 beams** across 30 base stations.
 
-### Citation
+---
+
+## 📚 Citation
+
+If you use this dataset in your research, please cite:
+
+> **L. Fechete et al.**,  
+> *Goal-Oriented Time-Series Forecasting: Foundation Framework Design*,  
+> arXiv:2504.17493 (2025)
+
+---
 
-Please cite this paper if you intend to use this dataset for your research:
-> L. Fechete et al., Goal-Oriented Time-Series Forecasting: Foundation Framework Design, arXiv:2504.17493 (2025).
+## 🔗 Code Repository
 
-The code associated with the dataset is provided [here](https://github.com/netop-team/gotsf).
+The official codebase for working with this dataset is available here:  
+👉 [https://github.com/netop-team/gotsf](https://github.com/netop-team/gotsf)