i will have to explain a lot...
i will explain in short
in machine learning, there r several ready made models which we can modify parameters n make changes accordingly n test
linear regression model is not very good for predicting future stock prices
for that we need time series prediction models like arima and garch....
i know all these sound big names, etc.... but for me arima is like elliott waves and garch is like moving averages....
this is what google say...
ARIMA models are generally denoted as ARIMA (p,d,q) where p is the order of autoregressive model, d is the degree of differencing, and q is the order of moving-average model. ARIMA models use differencing to convert a non-stationary time series into a stationary one, and then predict future values from historical data.
The ARIMA methodology is a statistical method for analyzing and building a forecasting model which best represents a time series by modeling the correlations in the data. Owing to purely statistical approaches, ARIMA models only need the historical data of a time series to generalize the forecast and manage to increase prediction accuracy while keeping the model parsimonious.
Despite being parsimonious, there are multiple potential disadvantages to using ARIMA models. Most important of them stems from the subjectivity involved in identifying p and q parameters. Although autocorrelation and partial autocorrelations are used, the choice of p and q depend on the skill and experience of the model developer. Additionally, compared to simple exponential smoothing and the Holt Winters method, ARIMA models are more complex and thus, have lower explanatory power.
Lastly, similar to all forecasting methods, by being backward looking, ARIMA models are not good at long term forecasts and are poor at predicting turning points. They can also be computationally expensive.
Thus, ARIMA models can be easily and accurately used for short-term forecasting with just the time series data, but it can take some experience and experimentation to find an optimal set of parameters for each use case.
Heteroskedasticity (GARCH) is a statistical model used in analyzing time-series data where the variance error is believed to be serially autocorrelated. GARCH models assume that the variance of the error term follows an autoregressive moving average process.
anyone can say nifty will go here or there.... n it will go also... but timing the market is most important.... n that is what i m trying to do by using gann time calculations with EW with help of machine learning models
just search in google, arima stock predictions
that will be more helpful
good luck
