nexoncn.com

文档资料共享网 文档搜索专家

文档资料共享网 文档搜索专家

http://www.chinaacc.com/

Time Series Analysis

ACCA P3 考试：Time Series Analysis 1. Uses The use of time series models is twofold： To obtain an understanding of the underlying forces and structure that produced the observed data point； To fit a model and proceed to forecasting, monitoring or even feedback and feed-forward control. Time series analysis is used for many applications, such as： Economic forecasting； Sales forecasting； Budgetary analysis； 2. Components Most time series patterns can be described in terms of two basic components： trend and seasonal variation （“seasonality”）. Trend describes a direction of change in the data that tends to occur at a similar rate over the short run. Within a long-term trend, data may change at varying rates or even reverse direction for short periods before continuing at the trend rate. After introducing a new product, for example, a firm may see sales grow slowly, followed by exponential growth as the new product catches on. Seasonality describes calendar-related effects such as sales preceding certain holidays, air miles flown during vacation seasons, etc. Cyclicality is another component. It arises when data plots in a repeating pattern around the trend line over a period lasting more than one year （e.g. economic expansion and contraction） . Business cycles are notoriously difficult to forecast, so they are often combined with trend effects into “trend-cycle” analysis. Trend and seasonality components may coexist in real-life data. For example, sales of a company can grow over years but still follow consistent seasonal patterns （e.g. 25％ of sales each year are made in December and only 4％ in August）. In many cases it is necessary to establish a trend for a series and then adjust each new data point for seasonality. This may be done monthly, quarterly or semi-annually （depending on the review period over which management will compare actual results to forecast）. Various methods exist for removing seasonal and cyclical noise from data and to make forecasts： Random walk： Next period's prediction is based on the latest actual. Because the data move up and down due to non-trend factors, however, this method may place too much emphasis on the latest actual result. Simple moving average： Next period's prediction is based on the latest moving average of n values for the series. Stock market analysis； Process and quality control； Inventory studies and workload projections.

中华会计网校 会计人的网上家园 www.chinaacc.com

1

http://www.chinaacc.com/

Weighted moving average：* Weights are assigned to observations, such that more recent results may be given more weight than older results. This still suffers from other problems with the simple moving average method, but can be another improvement over the random walk. It may be better to select equal weights for highly variable series. Exponential smoothing： Weights are assigned to last period's actual result using the “smoothing constant” and to last period's forecast amount （1 minus the smoothing constant） . Because the forecast value for the current period is the weighted actual value plus weighted forecast value of the prior period, this method implicitly gives weight to all actual values in determining the next period forecast.

中华会计网校 会计人的网上家园 www.chinaacc.com

2

赞助商链接

更多相关文档:

Lab 6 *Time* *series* *analysis* - Lab 6: *Time* *Series* *Analysis* Purpose: The purpose of this lab is to e...

using regression *analysis*, fuzzy c-means clustering, and *time* *series* ...Key words: agricultural circular economy development; *Time* *series* model; ...

using regression *analysis*, fuzzy c-means clustering, and *time* *series* ...Key words: agricultural circular economy development; *Time* *series* model; ...

1683(2014)04?0035?03 Application of *Time* *Series* *Analysis* on the Annual Precipitation of Zhengzhou city LVU Zhi?tao1,2 (1.College of Resources and ...

Chapter 4 *Time* *Series* *Analysis* *Time* *Series* (时间序列) Computing (计算) *Analysis* (分析) 4.1 *Time* *Series* 4.1.1 concept A *time* *series* is a set of ...

CHAPTER 15 *Time* *Series* *Analysis* page 651 15. Introduction page 651 Chapter 15 deals with the basic components of *time* *series*, *time* *series* decomposition,...

using regression *analysis*, fuzzy c-means clustering, and *time* *series* ...Key words: agricultural circular economy development; *Time* *series* model; ...

Short_Term_forecasting_Stock_Prize_using_*Time*_*Series*_*Analysis*_with_R_计算机软件及应用_IT/计算机_专业资料。Short Term forecasting Stock Prize using *Time* ...

Shenyang:Northeastern University Press,2001:334.(in Chinese) [2] Holger Kantz,Thomas Schreiber.Nonlinear *time* *series* *analysis*:second edition[M],Cambridge ...

更多相关标签:

相关文档

- analysis of financial time series-7
- Time-Series Data Analysis
- analysis of financial time series-8
- NONLINEAR TIME SERIES ANALYSIS
- non-stationary time series analysis
- analysis of financial time series-4
- non-stationary time series analysis non-stationary time series analysis
- Time_Series_Analysis_Intro
- Time Series Analysis Using Wavelets and Entropy Analysis
- Multifractal detrended fluctuation analysis of nonstationary time series
- R语言
- Chapter11__Handbook of Time Series Analysis
- Detrended Fluctuation Analysis of Autoregressive Processes
- An Analysis of a Bivariate Time Series in Which the Components Are Sampled at Different Ins

文档资料共享网 nexoncn.com
copyright ©right 2010-2020。

文档资料共享网内容来自网络，如有侵犯请联系客服。email:zhit325@126.com