Does anyone here have experience with using ML models to predict markets? I've found it very challenging so far, and I need help. This is how far I've gotten: Plots at the top, in light green background, are predictions using training data. Plots at the bottom, in light blue background, are predictions using testing data. Blue lines are historical prices of a stock/cryptocurrency. Red lines are predicted future 5 minute prices, made at time at which the blue line ends.
I tried a fun little project to explore some of the possibilities of GANs and the results came out pretty good so I figured I'd share. The goal was to create a network which can take extremely simple images composed of various color regions (where each color corresponds to a particular type of terrain) and generate a realistic looking terrain map.
Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that use a weighted sum of the input, like linear regression, and algorithms that use distance measures, like k-nearest neighbors. Standardizing is a popular scaling technique that subtracts the mean from values and divides by the standard deviation, transforming the probability distribution for an input variable to a standard Gaussian (zero mean and unit variance). Standardization can become skewed or biased if the input variable contains outlier values. To overcome this, the median and interquartile range can be used when standardizing numerical input variables, generally referred to as robust scaling.
Linear regression is a common technique used in the association study between the targeted outcome and some potential risk factors (e.g., age, sex). The violation of the normality assumption sometimes may be attributed by the skewed nature of the dependent variable and may be a concern for naturally skewed outcome variables, such as best corrected visual acuity, 1 refractive error, 2 and Rasch score. Normality violation will affect the estimates of the standard error (SE) and the confidence interval, and hence the significance of the risk factors. Nonparametric regression model or bootstrap techniques are suggested to be performed as they provide more robust estimates of SE. However, nonparametric techniques require large sample sizes to supply; the model structure, and are very sensitive to the outliers.