
# SMS Time series data for traffic and fraud forecasting

This dataset contains various time series from vendors.  

# Vendor A: 01.03.23-14.08.23

* TS_*_all - Count of all SMS
![](./images/A_all_01.03.23-14.08.23_hourly.png)
![](./images/A_all_01.03.23-14.08.23_15m.png)

# Vendor A: January

* TS_*_fraud - Count of fraud
![](./images/A_fraud_hourly.png)
![](./images/A_fraud_15m.png)
* TS_*_all - Count of all SMS
![](./images/A_all_hourly.png)
![](./images/A_all_15m.png)
* TS_*_hlrDelay - Mean values of hlr delay
![](./images/A_delay_hourly.png)
![](./images/A_delay_15m.png)

# Vendor B: January 1-8

* 1-8_TS_*_fraud - Count of fraud
![](./images/fraud_hourly.png)
![](./images/fraud_15m.png)
* 1-8_TS_*_all - Count of all SMS
![](./images/all_hourly.png)
![](./images/all_15m.png)
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
![](./images/delay_hourly.png)
![](./images/delay_15m.png)
task_categories:time-series-forecasting license:gpl-3.0 SMS fraud forecasting region:us
{"license": "gpl-3.0", "task_categories": ["time-series-forecasting"], "pretty_name": "SMS time series", "tags": ["SMS", "fraud", "forecasting"]}
TAGS
#task_categories-time-series-forecasting #license-gpl-3.0 #SMS #fraud #forecasting #region-us 


# SMS Time series data for traffic and fraud forecasting

This dataset contains various time series from vendors.  

# Vendor A: 01.03.23-14.08.23

* TS_*_all - Count of all SMS
![](./images/A_all_01.03.23-14.08.23_hourly.png)
![](./images/A_all_01.03.23-14.08.23_15m.png)

# Vendor A: January

* TS_*_fraud - Count of fraud
![](./images/A_fraud_hourly.png)
![](./images/A_fraud_15m.png)
* TS_*_all - Count of all SMS
![](./images/A_all_hourly.png)
![](./images/A_all_15m.png)
* TS_*_hlrDelay - Mean values of hlr delay
![](./images/A_delay_hourly.png)
![](./images/A_delay_15m.png)

# Vendor B: January 1-8

* 1-8_TS_*_fraud - Count of fraud
![](./images/fraud_hourly.png)
![](./images/fraud_15m.png)
* 1-8_TS_*_all - Count of all SMS
![](./images/all_hourly.png)
![](./images/all_15m.png)
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
![](./images/delay_hourly.png)
![](./images/delay_15m.png)
# SMS Time series data for traffic and fraud forecasting

This dataset contains various time series from vendors. # Vendor A: 01.03.23-14.08.23

* TS_*_all - Count of all SMS
![](./images/A_all_01.03.23-14.08.23_hourly.png)
![](./images/A_all_01.03.23-14.08.23_15m.png) # Vendor A: January

* TS_*_fraud - Count of fraud
![](./images/A_fraud_hourly.png)
![](./images/A_fraud_15m.png)
* TS_*_all - Count of all SMS
![](./images/A_all_hourly.png)
![](./images/A_all_15m.png)
* TS_*_hlrDelay - Mean values of hlr delay
![](./images/A_delay_hourly.png)
![](./images/A_delay_15m.png) # Vendor B: January 1-8

* 1-8_TS_*_fraud - Count of fraud
![](./images/fraud_hourly.png)
![](./images/fraud_15m.png)
* 1-8_TS_*_all - Count of all SMS
![](./images/all_hourly.png)
![](./images/all_15m.png)
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
![](./images/delay_hourly.png)
![](./images/delay_15m.png)
TAGS
#task_categories-time-series-forecasting #license-gpl-3.0 #SMS #fraud #forecasting #region-us 
 # SMS Time series data for traffic and fraud forecasting

This dataset contains various time series from vendors. # Vendor A: 01.03.23-14.08.23

* TS_*_all - Count of all SMS
![](./images/A_all_01.03.23-14.08.23_hourly.png)
![](./images/A_all_01.03.23-14.08.23_15m.png) # Vendor A: January

* TS_*_fraud - Count of fraud
![](./images/A_fraud_hourly.png)
![](./images/A_fraud_15m.png)
* TS_*_all - Count of all SMS
![](./images/A_all_hourly.png)
![](./images/A_all_15m.png)
* TS_*_hlrDelay - Mean values of hlr delay
![](./images/A_delay_hourly.png)
![](./images/A_delay_15m.png) # Vendor B: January 1-8

* 1-8_TS_*_fraud - Count of fraud
![](./images/fraud_hourly.png)
![](./images/fraud_15m.png)
* 1-8_TS_*_all - Count of all SMS
![](./images/all_hourly.png)
![](./images/all_15m.png)
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
![](./images/delay_hourly.png)
![](./images/delay_15m.png)
passage: TAGS
#task_categories-time-series-forecasting #license-gpl-3.0 #SMS #fraud #forecasting #region-us 
# SMS Time series data for traffic and fraud forecasting

This dataset contains various time series from vendors.# Vendor A: 01.03.23-14.08.23

* TS_*_all - Count of all SMS
![](./images/A_all_01.03.23-14.08.23_hourly.png)
![](./images/A_all_01.03.23-14.08.23_15m.png)# Vendor A: January

* TS_*_fraud - Count of fraud
![](./images/A_fraud_hourly.png)
![](./images/A_fraud_15m.png)
* TS_*_all - Count of all SMS
![](./images/A_all_hourly.png)
![](./images/A_all_15m.png)
* TS_*_hlrDelay - Mean values of hlr delay
![](./images/A_delay_hourly.png)
![](./images/A_delay_15m.png)# Vendor B: January 1-8

* 1-8_TS_*_fraud - Count of fraud
![](./images/fraud_hourly.png)
![](./images/fraud_15m.png)
* 1-8_TS_*_all - Count of all SMS
![](./images/all_hourly.png)
![](./images/all_15m.png)
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
![](./images/delay_hourly.png)
![](./images/delay_15m.png)