making business predictions.
past time series forecasts and make business decisions for the future
method of exploring and analyzing time-series data recorded or collected over a set period of time
Any data fit for time series forecasting should consist of observations over a regular, continuous interval.
gradual change in the time series data. The trend pattern depicts long-term growth or decline.
baseline values for the series data if it were a straight line.
short-term patterns that occur within a single unit of time and repeats indefinitely.
irregular variations and is purely random
Autoregressive Integrated Moving Average.
forecast corresponds to a linear combination of past values of the variable
linear combination of past forecast errors
adding a linear combination of seasonal past values and forecast errors.
models the next step in each time series using an AR model
redicting multiple time series variables using a single model.
Long Short Term Memory network or LSTM
ecurrent neural network that deals with long-term dependencies
can remember information from past data and is capable of learning order dependence in sequence prediction problems.
p = Number of autoregressive terms (AR
d = How many non-seasonal differences are needed to achieve stationarity (I)
q = Number of lagged forecast errors in the prediction equation (MA
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