Exponential smoothing techniques on daily temperature level data

The changes of temperature level occur throughout the year.This event whether hot temperature or cold temperature can affect human life and nature. Such event is also known as extreme event due to the nature of the data produced.Usually the time series of extreme dataset is rarely linear.The existence of nonlinear pattern and high fluctuation in variation greatly affect the quality of forecasting performances.Three exponential smoothing techniques have been tested to study their ability in handling of temperature level data from three cities in Texas.Single Exponential Smoothing Technique (SEST), Double Exponential Smoothing Technique (DEST) and Holt’s method were explored in preparing the temperature data.From the experiments, it was found that DEST is the most suitable technique to deal with the data compared to SEST and Holt's method.

[1]  V. Fthenakis.  The resilience of PV during natural disasters: The hurricane Sandy case , 2013, 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC).

[2]  Wei Zhong Zhang,et al.  Forecasting of Landslide Displacement Based on Exponential Smoothing and Nonlinear Regression Analysis , 2013 .

[3]  Pang Liang,et al.  The Application of Grey Model to the Prediction of Extreme Sea States Induced by Typhoon , 2013 .

[4]  Chang Jiang Zheng,et al.  Forecast of Bus Passenger Traffic Based on Exponential Smoothing and Trend Moving Average Method , 2013 .

[5]  Marco Zennaro,et al.  On Real-Time Performance Evaluation of Volcano-Monitoring Systems With Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[6]  Sarimah Abdullah,et al.  Application of univariate forecasting models of tuberculosis cases in Kelantan , 2012, 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE).

[7]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

[8]  Ajang Tajdini,et al.  Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models , 2015 .

[9]  Ana Maria Zambrano,et al.  Distributed Sensor System for Earthquake Early Warning Based on the Massive Use of Low Cost Accelerometers , 2015, IEEE Latin America Transactions.

[10]  Albert Orwa Akuno,et al.  Statistical Models for Forecasting Tourists’ Arrival in Kenya , 2015 .

[11]  T. Nishimura,et al.  Traffic prediction for mobile network using Holt-Winter’s exponential smoothing , 2007, 2007 15th International Conference on Software, Telecommunications and Computer Networks.