• Aim of the project:
Stabilise the device temperature over a period of time based on the historical data to provide a ideal styling conditions to
the user.
• Accomplishments:
– Carried out the data extraction from the device and performed data analysis on it.
– Optimized the parameter values for the ARIMA (non-seasonal),Auto-ARIMA, SARIMAX (seasonal) and LSTM model for better
accuracy.
– Able to forecast for a very high frequency data (millisecond’s), which is impressive.
• Deliverable:
Stable temperature is ideal for hair styling, this framework successfully predicts the future power output (correlated with
the temperature) to reduce the time lag.
• Breif results:
1. ARIMA:
2. Auto-ARIMA and SARIMAX:
3. LSTM:
arunsinghbabal/Time-Series-Predictive-Analytics-for-Hair-Device
Stable temperature is ideal for hair styling, this framework successfully predicts the future power output (correlated with the temperature) based on the historical data to reduce the time lag.
Jupyter Notebook