HTime series using Holt-Winters Forecasting Procedure
Summary
The Holt-Winters forecasting procedure is a simple widely used projection method which can cope with trend and seasonal variation. We can apply this method to lots of fields such as banking data analysis, investment forecasting, inventory controlling and so on. This paper shows us a practical banking credit card example using Holt-Winter method in Java programming for data forecasting. The reason we use Holt-Winter is that this method is simple while generally works well in practice, and is particularly suitable for producing short-term forecasts for sales or demand time-series data. Theorem

There are two types of seasonal model: an additive version which assumes that the seasonal effects are of constant size and a multiplicative version which assumes that the seasonal effects are proportional in size to the local deseasonalized mean level. Both seasonal models assume that the local deseasonalized mean level may be modified by an additive trend term and also that there is an additive error term of constant variance. Suppose we have an observed time series, denoted by X1, X2, …, Xn , and wish to forecast Xn+k. The forecast made at time n for k steps ahead will be denoted by Xnk. For a univariate forecast this depends only on Xn, Xn-1,…… In simple exponential smoothing, the one-step-ahead predictor can be written in the recurrence form Xt(1)= Lt+ Tt+ It-p+1

Where the smoothing parameter, α, is usually constrained so that 0 < α <1. The Holt-Winters method (sometimes called the Winters method or seasonal exponential smoothing) generalizes this approach to deal with trend and seasonality. Let α, γ, δ denote three smoothing parameters and let p denote...

...TIMESERIES ANALYSIS
Introduction
Economic and business timeseries analysis is a major field of research and application. This analysis method has been used for economic forecasting, sales forecasting, stock market analysis and company internal control. In this paper, we will talk about timeseries and review techniques that are useful for analyzing timeseries...

...Timeseries
In statistics, signal processing, econometrics and mathematical finance, a timeseries is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of timeseries are the daily closing value of the Dow Jones index or the annual flow volume of the Nile River at Aswan. Time...

....2.3 Timeseries models
Timeseries is an ordered sequence of values of a variable at equally spaced time intervals. Timeseries occur frequently when looking at industrial data. The essential difference between modeling data via timeseriesmethods and the other methods is that Timeseries analysis accounts...

...Forecasting Models: Associative and TimeSeries
Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning.
TimeSeries and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market...

...TIMESERIES ANALYSIS
Chapter Three
Univariate TimeSeries Models
Chapter Three
Univariate timeseries models c WISE
1
3.1
Preliminaries
We denote the univariate timeseries of interest as yt.
• yt is observed for t = 1, 2, . . . , T ;
• y0, y−1, . . . , y1−p are available;
• Ωt−1 the history or information set at time t − 1.
Call such a...

...TimeSeries Models for Forecasting New One-Family Houses Sold in the United States
Introduction
The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over-leveraged positions in the mortgage backed securities market. Credit became widely...

...TIMESERIES MODELS
Timeseries analysis provides tools for selecting a model that can be used to forecast of future events.
Timeseries models are based on the assumption that all information needed to generate a forecast is contained in the timeseries of data. The forecaster looks for patterns in the data and tries to obtain a forecast by projecting that pattern into the...

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