New answers tagged arima
0
votes
Time series model without ARMA component and with exogenous variables
what you have is fine ( no ARIMA terms necessary ) but three things to consider:
should the X's be lagged by one time unit ? or do they really occur so that the current X's influence the current 𝑌 ?
...
- 2,307
4
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Manually compute ARIMAX forecast
Your manual computation computes an ARIMAX forecast. Unfortunately, that is not what arima() models. Rather, arima() models and ...
- 117k
4
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Multiple predictions using ARIMA
Overall, your approach makes sense.
One point I would definitely change: move step 2 (which I presume refers to SARIMA order selection) either between step 6 and step 7, or into step 7.
Why? Selecting ...
- 117k
1
vote
Why should we remove trend and seasonality before forecasting?
I can think of two reasons:
It is often useful to decompose a hard problem into several minor issues which are easier solve.
Since we can often think of a time series being composed by several ...
- 191
3
votes
Accepted
ARIMA - Identifying an outlier in residuals
The plot does not show residuals. It shows the autocorrelation function (ACF) of the residuals, i.e. the values of autocorrelation $\text{Corr}(u_t,u_{t-h})=1$ for a numer of different lags $h$. ...
- 64.7k
0
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Accepted
SARIMAX.predict() and SARIMAX.forecast() exog? Does exog need to be preknown for predict()?
It depends on how you set up your model. To take a very special case of zero-mean ARX(1), it can be
$$
y_t=\varphi_1 y_{t-1}+\beta x_t+u_t
$$
or
$$
y_t=\varphi_1 y_{t-1}+\beta x_{t-1}+v_t.
$$
In the ...
- 64.7k
1
vote
Let $X_t$ be an ARIMA(1,1,1) process and $Y_t = Y_{t-1} + X_t$. What kind of process is $Y_t$?
The process $Y_t$ is the integration of $X_t$
$$Y_t = \sum_{-\infty}^t X_t$$
So you have one more integration step and that means that it is an ARIMA(1,2,1) process.
Indeed, the recursive formula can ...
- 71.8k
4
votes
Accepted
Role of `trend` argument compared to integral order in ARIMA model
ARIMA Elements:
AR (AutoRegressive): Uses lag to measure the correlation between observations; in other words, it uses past values in the series to predict future values.
I (Integrated): that data has ...
- 56.7k
1
vote
How is forecasting values for stationary time series even possible?
I very much recommend section 1.4 in Shumway & Stoffer, Time Series Analysis and Its Applications (4th ed.). They introduce strict stationarity and weak stationarity, and immediately say that by &...
- 117k
4
votes
Accepted
Do I need to take the residuals of the ARMA fit for my linear regression?
ARMA is a pure time series model, i.e. it doesn't have exogenous variables $X_t$ unlike a typical regression $y_t=X_t\beta+\varepsilon_t$. In a generic formulation of ARMA such as $\phi(B)y_t=\theta(B)...
- 60.1k
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