Monkey Forecast 

 

A Naive Forecasting Model is a special case of the moving average forecasting model where the number of periods used for smoothing is 1.

Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period in the future. It is not at all useful as a medium-long range forecasting tool.

 

Simple Exponential Smoothing Forecast Model

A simple exponential smoothing forecast model is a very popular model used to produce a smoothed Time Series. Whereas in simple Moving Average models the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observations get older.

In other words, recent observations are given relatively more weight in forecasting than the older observations.

 

 

Double exponential smoothing - also known as Holt exponential smoothing - is a refinement of the popular simple exponential smoothing model but adds another component which takes into account any trend in the data. Simple exponential smoothing models work best with data where there are no trend or seasonality components to the data. When the data exhibits either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any trend in the data

A single variable polynomial regression model essentially attempts to put a polynomial line - a curve if you prefer - through the data points. Mathematically, assuming the independent variable is x and the dependent variable is y, then this line can be represented as:  y = a₀ + a₁*x + a₂*x² + a₃*x³ + ... + a_m*x^m

Autoregressive integrated moving average (ARIMA) model is a generalisation of an autoregressive moving average or (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series. The model is generally referred to as an ARIMA(p,d,q) model where p, d, and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated, and moving average parts of the model respectively. A number of variations on the ARIMA model are commonly used. For example, if multiple time series are used then the Xt can be thought of as vectors and a VARIMA model may be appropriate. Sometimes a seasonal effect is suspected in the model. For example, consider a model of daily road traffic volumes. Weekends clearly exhibit different behaviour from weekdays. In this case it is often considered better to use a SARIMA (seasonal ARIMA) model than to increase the order of the AR or MA parts of the model. If the time-series is suspected to exhibit long-range dependence then the d parameter may be replaced by certain non-integer values in a Fractional ARIMA (FARIMA also sometimes called ARFIMA) model.

 

1. Progressive Model
Polynomial Regression 的方法. 佢會用之前數據推測一條Formula，最Fit之前的走勢.
y = a0 + a1*x + a2*x2 + a3*x3 + ... + am*xm
跟住用條Formula 去計未來幾日之股價．　如果之後布股價有大起落，Formula會改變來Fit番之前的g走勢．　從而提供更準確同Update的預測．　
如果參考之前預測的準確性，可到 http://www.monkeyforecast.com/fcstreview.htm 會顯示十日之前的預測和實際股價的差異．從而用戶可決定各種Model　對唔同股票的適用性．

2. Double Exponential Smoothing
Double Exponential Smoothing 採用了Formula 推算走勢.
e.g. S3 = ay2 + (1-a)*S2 S3是推算出來的未來股價, y 是昨天的實際股價, s2是昨天的估計股價. a是偏差值.經過一串計算來推算未來股價.

3. 5-days Moving Average
有時候,粗略的準確比精細的錯誤更合用.所以我也用了採用五天的股價平均值來推算走勢.類似常用的10天平均線或20天線. 只不過多了預測功能.


由於Monkey並非神算.只是在傳統 Technical Analysis 工具之外再提供一種相對科學化的工具給大家參考. 請再加上自己的分析.