Hello All:wave:

I am looking for research help:yup:

There is a data set and the goal is to build a model.

There are events that happen. 229 of these events have been recorded and more of these events will happen in the future.

Each event has an outcome. outcome is recorded for the previous 229 events and the goal is to predict outcomes in the future events and hopefully with much certainty. It is not critical to make a prediction every time but it is important to be certain.

Each event's outcome is being predicted during a time interval and this variable is segmented as 0-100% and it signifies the timeline of each prediction made.

There are 229 stocks from the stock market that have been recorded. the goal is to predict what will be the opening price next morning.

Throughout yesterday's day, at time intervals when volume significantly changed, price of stocks was recorded. This was done until stock market closed. Also this morning's price is recorded as Open_Price

Variable View:

Point_Time_segment is a created variable from Point_Time

First data point was recorded as 1 divide by (the number of observations for that stock), second point was 2 divide by (the number of observations for that stock). And closing data point's Point_Time_Segment = 1

In a sense, there are 229 of the following graphs. The red point on the pictures is the opening price next day. This is the value to predict. But again the goal is simply to be certain on whether the price will be higher or lower than close price.

I am looking for research help:yup:

There is a data set and the goal is to build a model.

There are events that happen. 229 of these events have been recorded and more of these events will happen in the future.

Each event has an outcome. outcome is recorded for the previous 229 events and the goal is to predict outcomes in the future events and hopefully with much certainty. It is not critical to make a prediction every time but it is important to be certain.

Each event's outcome is being predicted during a time interval and this variable is segmented as 0-100% and it signifies the timeline of each prediction made.

**Example**There are 229 stocks from the stock market that have been recorded. the goal is to predict what will be the opening price next morning.

Throughout yesterday's day, at time intervals when volume significantly changed, price of stocks was recorded. This was done until stock market closed. Also this morning's price is recorded as Open_Price

Variable View:

*StockID, Numeric, 1-229*

Open_Price, Numeric

Point_Time, Date

Point_Price, Numeric

Point_Time_Segment, Numeric, (0-1]

Open_Price, Numeric

Point_Time, Date

Point_Price, Numeric

Point_Time_Segment, Numeric, (0-1]

Point_Time_segment is a created variable from Point_Time

First data point was recorded as 1 divide by (the number of observations for that stock), second point was 2 divide by (the number of observations for that stock). And closing data point's Point_Time_Segment = 1

*The goal is to predict whether tomorrow morning's opening price will be higher or lower than close price, and to do this with most certainty.*In a sense, there are 229 of the following graphs. The red point on the pictures is the opening price next day. This is the value to predict. But again the goal is simply to be certain on whether the price will be higher or lower than close price.

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