## An introduction to stock market trend analysis

Stock market trend analysis or equity market trend analysis refers to the process of examining the current trends based on the past and current movement of the stocks in order to predict future trends.

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## How to test time series autocorrelation in STATA?

This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.

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## Lag selection and cointegration test in VAR with two variables

The previous article showed that the three-time series values Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC) and Private Final Consumption (PFC) are non-stationary. Therefore they may have long-term causality. The general assumption, in this case, is that consumption PFC affects GDP, therefore these variables might be cointegrated.

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## How to predict and forecast using ARIMA in STATA?

After performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) .

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## ARIMA modeling for time series analysis in STATA

In the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for the time series GDP were identified as under. S. No ARIMA 1 (1,1,1) 2 (1,1,2) 3 (1,1,3) 4 (1,1,4) 5 (1,1,5) 6 (1,1,6) 7 (1,2,1) 8 (4,2,1) 9 (9,2,1)  Table 1: ARIMA models as per ACF and PACF graphs.

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## How to apply missing data imputation?

Missing data is one of the most common problems in almost all statistical analyses. If the data is not available for all the observations of variables in the model, then it is a case of ‘missing data’.

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## Getting acquainted with neural network analysis

Neural network, popularly known as Artificial Neural Network (ANN) is an information processing system with a large number of nodes and connections as part of a structure which helps in processing complex information.

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## How to detect outliers in a dataset?

Outliers are those data points which are distant from the other observations in the data set. They can be either because of the variability in the data set or due to measurement errors.

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