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ARIMA
This project forked from josephmagiya/auto-arima
ARIMA
Dataset - https://drive.google.com/file/d/1bypF5zuUJUP1nLLfMQqac5WEQ_XAtoBK/view?usp=sharing For more details - https://link.medium.com/8zMSqFI16T ARIMA An ARIMA model is a class of statistical models for analyzing and forecasting time series data. It explicitly caters to a suite of standard structures in time series data, and as such provides a simple yet powerful method for making skillful time series forecasts. AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations. I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary. MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations. The parameters of the ARIMA model are defined as follows: p: The number of lag observations included in the model, also called the lag order. d: The number of times that the raw observations are differenced, also called the degree of differencing. q: The size of the moving average window, also called the order of moving average. A linear regression model is constructed including the specified number and type of terms, and the data is prepared by a degree of differencing in order to make it stationary, i.e. to remove trend and seasonal structures that negatively affect the regression model. A value of 0 can be used for a parameter, which indicates to not use that element of the model. This way, the ARIMA model can be configured to perform the function of an ARMA model, and even a simple AR, I, or MA model. An ARIMA model can be created using the statsmodels library as follows: - Define the model by calling ARIMA() and passing in the p, d, and q parameters. - The model is prepared on the training data by calling the fit() function. - Predictions can be made by calling the predict() function and specifying the index of the time or times to be predicted. We can automate the process of evaluating a large number of hyperparameters for the ARIMA model by using a grid search procedure. We can automate the process of training and evaluating ARIMA models on different combinations of model hyperparameters. In machine learning this is called a grid search or model tuning. The approach is broken down into two parts: 1. Evaluate an ARIMA model. 2. Evaluate sets of ARIMA parameters. 1. Evaluate an ARIMA model. This approach involves the following steps: 1. Split the dataset into training and test sets. 2. Walk the time steps in the test dataset. a.Train an ARIMA model. b.Make a one-step prediction. c.Store prediction; get and store actual observation. 3. Calculate error score for predictions compared to expected values. The dataset is split in two: 66% for the initial training dataset and the remaining 34% for the test dataset. Each time step of the test set is iterated. Just one iteration provides a model that you could use to make predictions on new data. The iterative approach allows a new ARIMA model to be trained each time step. A prediction is made each iteration and stored in a list. This is so that at the end of the test set, all predictions can be compared to the list of expected values and an error score calculated. In this case, a mean squared error score is calculated and returned. 2. Evaluate sets of ARIMA parameters. Specify a grid of p, d, and q ARIMA parameters to iterate. A model is created for each parameter and its performance evaluated by calling the evaluate_arima_model() function described in the previous section. There are two additional considerations. -The first is to ensure the input data are floating point values (as opposed to integers or strings), as this can cause the ARIMA procedure to fail. -Second, the statsmodels ARIMA procedure internally uses numerical optimization procedures to find a set of coefficients for the model. These procedures can fail, which in turn can throw an exception. We must catch these exceptions and skip those configurations that cause a problem. This happens more often then you would think The Least MSE is the best set of parameters.
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