Open AccessComparison of Forecasting Techniques for Short-term and Long-term Real Life Problems
Author(s) -
Nandita Barman,
M Babul Hasan
Publication year - 2017
Publication title -
the dhaka university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2408-8528
pISSN - 1022-2502
DOI - 10.3329/dujs.v65i2.54523
Subject(s) - exponential smoothing , term (time) , multiplicative function , series (stratigraphy) , econometrics , autoregressive integrated moving average , statistics , linear regression , moving average , sample (material) , time series , exponential function , computer science , mathematics , mathematical analysis , paleontology , physics , chemistry , chromatography , quantum mechanics , biology
In this paper, we analyze the most appropriate short-term and long term forecasting methods for our practical life where several methods of time series forecasting are available such as the Moving Averages method, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. This paper mainly concentrates on the Holt- Winters Exponential Smoothing technique as applied to time series that exhibit seasonality. The accuracy of the out-of-sample forecast is measured using MSE, MAPE, MAD. We will observe that the empirical results from the study indicate that the Holt-Winter‘s Multiplicative Forecasting Method processes as the most appropriate forecasting method for the sets of real life data that will be analyzed.
Dhaka Univ. J. Sci. 65(2): 139-144, 2017 (July)