i)            MA(Moving Average)A moving average (MA) is a trend-following ora lagging indicator which is typically used in identifying the directionof the trends and in determining the support and resistance levels because itis based on past price movements. The two basic and commonly used MAs are Simple Moving Average; It is a simpleaverage of a security over a defined number of time periods, Exponential Moving Average; It gives moreweight age to more recent prices.  i)            BollingerBandsThis technical indicator is composed of a center line andtwo price channels normally called bands located above and below the line.Central line is the Exponential Moving Average and the bands are standarddeviations of the selected script.

The price channels/bands expands andcontracts depending of the price movement of that particular stock.Whenthe selected stock prices continually touch the upper Bollinger Band, thescript is thought to be overbought indicating the selling indication,conversely, when they continually touch the lower band, prices are thought tobe oversold, triggering a strong selling signal. Ina strong uptrend, prices usually fluctuate between the upper band and the20-day moving average. TimeSeries Analysis:Modern methods of time series forecasting are developedon the idea that past prices movements can be explain about its future behavior.  The approach ofbuilding a time series forecasting is to first specify a model. The model usedin forecasting generally composed of statistical formulation based on thedynamic relationships between variables that we infer from (also known asinformation set) and variables closely related to which we observe.

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The mostcommon approach of time series forecasting is derived from regression analysis.A regression model includes a linear parametric relation between an explanatoryvariables set and dependent variables. BothWiener (1949) and Kolmogorov (1941) are considered to bepioneers in linear prediction field while most of the techniques used today forlinear prediction differ from there models but it is evident that theirsolutions to same geometrical problems are still equivalent and providessimilar results.

Weiner’s work wasmostly relevant to today’s times series forecasting in his work he rigorouslyformulate the problem of “Signal extraction”; given observations on time seriesdegraded by additive noise. Given the historicalperspective of mass systems of equation models popular for macro-economicforecasters in mid-19th century it became apparent that theforecasting models derived from signal extraction forecasted similarly ascompared to those based on complicated systems of economic relationshipequations formed as individual, interconnected, classical regressions.