75.Sanjeev Kumar Khichi ONLINE.cdr Evaluation Steps Ÿ Association between the predictor and the predicted Ÿ Predictor variables - used to predict values of variable interest, sometimes called independent variables Ÿ Predicted variable = Dependent variable Ÿ Regression - technique for tting a line to a set of points Ÿ Linear regression is the most widely used form of regression Ÿ The objective is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations of data points from the line. Ÿ y = a + bx Ÿ Where Ÿ y = predicted (dependent) variable Ÿ x = predictor (independent) variable Ÿ b = slope of the line Ÿ a = value of y when x = 0 (the height of line at the y intercept) Given n data points, nd the intercept a and the slope b to Ÿ Time-ordered sequence of observations taken at regular intervals over a period of time Ÿ Future values of the series can be estimated from past values. Types of Variations in Time Series Data Ÿ Trend - long-term movement in data Ÿ Seasonality - short-term regular variations in data Ÿ Cycles – wavelike variations of long-term Ÿ Irregular variations - caused by unusual circumstances Ÿ Random variations - caused by chance Ÿ Forecast error:=Actual – Forecast =A(t-1)-F(t-1) Forecast today=Forecast yesterday+(alpha)*(Forecast error yesterday) Each new forecast is equal to the previous orecast plus a percentage of the previous error. Ÿ Step 1 - Smoothing the level of the series Ÿ Step 2 – Smoothing the trend STATISTICAL EVALUATION OF FORECASTING METHODS. Original Research Paper Sanjeev Kumar Khichi Associate Professor, Community Medicine, SHKM Govt. Medical College, Nalhar, Nuh, Haryana Medical Science In this paper some systematic steps were used for forecasting. Generate forecasts for data with different patterns: level, trend, seasonality, and cyclical.Describe causal modeling using linear regression Compute forecast accuracy .Explain how forecasting models should be selected, Decide what needs to be forecast.Level of detail, units of analysis & time horizon required.Evaluate and analyze appropriate data.Identify needed data.Select and test the forecasting model, Cost, ease of use & accuracy,Generate the forecast accuracy over time. ABSTRACT KEYWORDS : Dr.Nand Kishore Singh* Associate Professor& Statistician, Community Medicine, SHKM Govt. Medical College Nalhar, Nuh, Haryana *Corresponding Author Neha Nehra Research Scholar in Sun Rise University. Sarita Dahiya Assistant Professor, Psychology, Govt. College for Women, Rewari, Haryana å = -- = = n t tt bxay 1 2)( Minimize line thefrom deviations of sum theMinimize errors squared of sum theMinimize 2 11 2 1 1 1 ÷ ø ö ç è æ - - = åå å å å == = = = n t t n t t n t n t n t tttt xxn yxyxn b n x b n y a n t t n t t åå == -= 11 )( 111 --- -+= tttt FAFF a )Tα)(S(1αAS 1t1ttt -- +-+= 1t1ttt β)T(1)Sβ(ST -- -+-= 10 X GJRA - GLOBAL JOURNAL FOR RESEARCH ANALYSIS VOLUME-9, ISSUE-3, MARCH-2020 • PRINT ISSN No. 2277 - 8160 • DOI : 10.36106/gjra Y=a + bX Forecast Software Ÿ Spreadsheets Ÿ Microsoft Excel, Quattro Pro, Lotus 1-2-3 Ÿ Limited statistical analysis of forecast data Ÿ Statistical packages Ÿ SPSS, SAS, NCSS, Minitab Ÿ Forecasting plus statistical and graphics Ÿ Specialty forecasting packages Ÿ Forecast Master, Forecast Pro, Autobox, SCA Guidelines for Selecting Software Ÿ Does the package have the features you want? Ÿ What platform is the package available for? Ÿ How easy is the package to learn and use? Ÿ Is it possible to implement new methods? Ÿ Do you require interactive or repetitive forecasting? Ÿ Do you have any large data sets? Ÿ Is there local support and training available? Ÿ Does the package give the right answers? ARIMA and SES model for forecasting DISCUSSION Three basic principles of forecasting are: forecasts are rarely perfect, are more accurate for groups than individual items, and are more accurate in the shorter term than longer time horizons. The forecasting process involves ve steps: decide what to forecast, evaluate and analyze appropriate data, select and test model, generate forecast, and monitor accuracy. Forecasting methods can be classied into two groups: qualitative and quantitative. Qualitative methods are based on the subjective opinion of the forecaster and quantitative methods are based on mathematical modeling. Time series models are based on the assumption that all information needed is contained in the time series of data. Causal models assume that the variable being forecast is related to other variables in the environment. There are four basic patterns of data: level or horizontal, trend, seasonality, and cycles. In addition, data usually contain random variation. Some forecast models used to forecast the level of a time series are: naïve, simple mean, simple moving average, weighted moving average, and exponential smoothing. Separate models are used to forecast trends and seasonality. A simple causal model is linear regression in which a straight- line relationship is modeled between the variable we are forecasting and another variable in then environment. The correlation is used to measure the strength of the linear relationship between these two variables. Three useful measures of forecast error are mean absolute deviation mean square error and tracking signal. There are four factors to consider when selecting a model: amount and type of data available, degree of accuracy required, length of forecast horizon, and pattern. REFERENCES 1. Kahneman Daniel; Tversky, Amos (1979). "Prospect Theory: An Analysis of Decision under Risk" (PDF). Econometrica. 47 (2): 263–291. 2. Jump up to:a b Kahneman, Daniel; Tversky, Amos (1977). "Intuitive prediction: Biases and corrective procedures" (PDF). Decision Research Technical Report PTR-1042-77-6. In K ahneman Daniel; T versky, Amos (1982). "Intuitive prediction: Biases and corrective procedures". In Kahneman, Daniel; Slovic, Paul; Tversky, Amos (eds.). Judgment Under Uncertainty: Heuristics and Biases. pp. 414 3. "Week 10: Reference Class Forecasting". Conceptually. Retrieved 20 April 2017. 4. "Outside view". Less Wrong Wiki. Retrieved 20 April 2017. 5. Flyvbjerg, Bent (2006). "From Nobel Prize to Project Management: Getting Risks Right". Project Management Journal. 37 (3): 5–15. arXiv:1302.3642. 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The Oxford Handbook of Project Management. å å - - = 22 XnX YXnXY b XbYa -= X 11GJRA - GLOBAL JOURNAL FOR RESEARCH ANALYSIS VOLUME-9, ISSUE-3, MARCH-2020 • PRINT ISSN No. 2277 - 8160 • DOI : 10.36106/gjra