Random Walk Process
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] A time series is said to follow a random walk process if the predicted value of the series in one period is equivalent to the value of the series in the previous period plus…
Coefficient Instability
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] Time series coefficient estimates can change over time. Regression coefficient estimates derived from an earlier sample period can differ from those approximated using a later period. Therefore, sample period selection is crucial in estimating…
Multiperiod Forecasts
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] In-sample Forecasts An in-sample forecast uses the fitted model to derive the predicted values within the period used to estimate model parameters. In-sample forecast errors are residuals generated from a fitted-time series model. For…
The Mean Reversion
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] Mean reversion refers to the behavior of a time series to fall when its values are above the mean and rise when they are below the mean. This is illustrated as follows: A mean-reverting…
Residual Autocorrelation
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] The autocorrelation of a time series refers to the correlation of that time series with its past values. The kth order autocorrelation is the autocorrelation between one time series observation and the value k…
Autoregressive Models and Multiperiod Forecasts
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] The current-time values of a time series are related to the previous time values. This property is termed autoregressive. Autoregressive models are abbreviated (\(AR_{p}\)) models. \(p\) is known as the order of the model….
Covariance Stationary Property
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] A time series is said to be covariance stationary if its properties, such as the mean and variance, remain constant over time. A time series that is nonstationary leads to invalid linear regression estimates…
Linear or Log-Linear Model
A linear trend model should model a time series that increases over time by a constant amount. On the other hand, a time series that grows at a constant rate should be modeled by a log-linear model. To decide between…
Predicted Trend Value of a Time Series
[vsw id=”-SilFtkpBK8″ source=”youtube” width=”611″ height=”344″ autoplay=”no”] A time series shows data on a variable’s outcome in different periods—for example, a time series showing EURUSD exchange rates between a given time interval. Time series models explain the past and predict the…
Evaluating the Fit of a Machine Learning Algorithm
[vsw id=”ifHmwpgHWYY” source=”youtube” width=”611″ height=”344″ autoplay=”no”] Model Training Suppose that the target variable (y) for the ML training model has the sentiment class labels (positive and negative). To ease the calculation of the performance metrics, we relabel them as 1…
