Questions tagged [forecasting]
Prediction of the future events. It is a special case of [prediction], in the context of [time-series].
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15 views
Minimum sample size for time series cross-validation (tsCV)
I am doing cross-validation of an autoregressive neural network model and I am using the tsCV function (forecast package) ...
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0answers
9 views
What are the confidene intervals when plotting the forecast function in R?
So I have forecasted with the 'forecast' function in Rob Hyndman's "Forecast" package using R. But when plotting it I am in doubt what the blue and grey area in the graph means. I assume that the blue ...
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1answer
29 views
Predict song genre using LSTM
I have a dataset of songs based on genres. For example, a song may hold {5, 2, 3} as scores set for Sentimental, Rock and Jazz. In total there are 800 songs sequentially arranged. I want to predict ...
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1answer
23 views
What is the meaning of an autoregressive parameter greater than one? [duplicate]
I have created a AR(2,1,0) model with the first two parameters equal to -1.08 and -0.33. I understand that a autoregressive parameter equal to 1 implies non-stationarity and a random walk process so I'...
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23 views
What is the equaton for ARIMAX(1,1,1) and how can I undifference the 1st differenced data to fit the equation? [duplicate]
I have generated the ARIMAX(1,1,1) model to predict the future Barramundi catch. In this model, there are two exogenous variables (price and streamflow) that affect Barramundi catch. I have used 1st ...
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7 views
Which bagging & boosting algorithms can be used for time series forecasting?
I have a dataframe as follows:
...
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14 views
Pricing transfer prices for oil hub? 390 Days of prices given
Need some input in how to attack this problem.
Given are 8 timeseries:
UK Oil price, Delivery Quarter 1 2020
UK Oil price, Delivery Quarter 2 2020
UK Oil price, Delivery Quarter 3 2020
UK Oil price, ...
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2answers
33 views
Differencing and trend in time series forecasting
I understand that a time series is differenced to remove trend. But if trend can be modeled for forecasting purposes then why difference a time series at all?
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1answer
136 views
What is the difference between an accuracy measure and an error metric?
The two concepts are distinct in measure theory. Nonetheless, moving out from measure theory, the two terms are often used interchangeably. To most forecasters, especially forecast practitioners, they ...
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1answer
22 views
How to do location forecasting on Chicago Crime Dataset?
I am using the dataset https://www.kaggle.com/currie32/crimes-in-chicago and given primary type of the crime I want to forecast the next location of crime. What approach should I follow ?
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0answers
27 views
How do you forecast ARIMA with multiple regressors? [migrated]
The complete R data and code for my question is here: https://pastebin.com/QtG6A7ZX.
I am new to R and still a beginner when it comes to time series analysis, so please forgive my ignorance.
I am ...
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0answers
10 views
How to interpret ARIMA(0,1,1)(1,0,0)[12] with drift from R? [duplicate]
The code that Î used to generate ARIMA summary is,
arimafore = forecast(auto.arima(sales), h = 24)
summary(arimafore)
and i got this output
Forecast method: ...
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0answers
27 views
Recurrence of $k$-step ahead forecast with ARMA
For brevity, let's consider an AR(1) model, but this question should apply to ARMA(p, q) in general. Assume we are at time $T$ and would like to forecast $k$ steps ahead,
$$
X_{T+k} = \phi_0 + \phi_1 ...
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1answer
48 views
Make daily business data stationary for ARIMA
For my master thesis I have a dataset with the daily count of orders from a company over ten years. Naturally this data follows strong seasonality with almost no orders on the weekend. To fit an ARIMA ...
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0answers
23 views
Is stationarity a requirement when using neural networks for time series forecasting?
I'm getting conflicting information on whether stationarity is a requirement when using neural networks for time series forecasting:
In this lecture, the speaker says it isn't a requirement.
In this ...
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0answers
20 views
What is the best model to forecast ACT scores using practice test scores and past student data?
I understand that I may not be asking this question correctly, and would appreciate any feedback possible in order to help set me on the path to figuring this out...
I work at a high school where ...
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1answer
234 views
Model for spatiotemporal and discrete variables
I have a situation where I am monitoring events at 50 or so geographical sites in a town and at each of these sites, I am making measurements regarding the count of certain particles (so the ...
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0answers
22 views
ARIMA predictors - clarification
I'm working on multivariate time series (still), and would like some clarification. I was reading this site: Duke Forecasting and I came across this statement:
"We see that the most significant ...
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1answer
26 views
acf and pacf suggests MA but auto.arima gave AR
I have this data which is residual series obtained from predicted values and observations. original series was a random walk with a very small drift(mean=0.0025).
...
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1answer
18 views
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0answers
20 views
How to properly add spatial features for a precipitation time series forecasting?
I am reading this paper.
The center of the circle is the site where the model should forecast precipitation. Red stars in the picture are nearby sites and each site has these features:
I want to ...
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1answer
48 views
Getting best fitted model using Auto ARIMA but prediction result is very bad
I saw this: time series - Poor prediction using ARIMA model
But the answers aren't clear and isn't directing to me for solving the problem I have. Using only AR is giving me better prediction whereas ...
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0answers
21 views
Forecast efficiency: why no correlation between errors and available information?
(Applied Economic Forecasting using Time Series methods; Ghysels, Marcellino, 2018), in the chapter about forecast evaluation, relates efficiency as "the efficient use of the available information". ...
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22 views
Kalman filter on stock sentiment time series
I was wondering if & how I can use a Kalman filter on my dataset which contains closing prices of stocks + sentiment scores of tweets about that stock for each day in a timeframe of 1 month.
e.g....
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0answers
36 views
How to choose the right forecast method for variable 'X' when I have some available forecast for variable 'Y' with historical data of X and Y?
I have yearly historical data for variables 'X' and 'Y'. Say the time frame is 't'. In addition to available historical data, I also have the forecast data of variable 'Y' for t+1. My aim is to ...
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3answers
253 views
Can my data be white noise if the mean >0?
According to the auto-correlation method, my time-series is white noise (i.e. 95% of ACF within ±2/√T), yet the data are counts and thus the mean >0.
Are these two facts incompatible?
I'm using the ...
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0answers
10 views
How to calculate forecast given variation by day
I'm trying to work through a problem and I'm wondering if I'm interpreting it correctly.
Let's say we predict the price of stock (today worth $50) to vary by N~(0,1) every day, and you are looking to ...
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0answers
12 views
How does forecast skill score change when seasonality in the forecast quantity is removed?
Given RMSE skill score $s$:
\begin{equation}\label{eq:msess}
s = 1-\frac{\text{RMSE}(f,x)}{\text{RMSE}(r,x)},
\end{equation}
where
$f$, $r$, and $x$ are forecasts of interest, reference forecasts, ...
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2answers
36 views
Fitting a GARCH model and forecast using validation set approach In R
I have seperated the data into training and testing data. Then I fitted this simple garch model for training data as follows,(using rugarch package)
...
4
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2answers
97 views
Time Series: Confused about identification of (possibly?) an ARMA(p,q) model
this is my first ever question on a website i use frequently!
This time series has given me much trouble over the last couple of days even after extensive googling, I suppose with TS theres no two ...
2
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1answer
34 views
What is the difference between probabilistic forecasting and quantile forecasting?
A probabilistic time series forecast outputs the entire distribution of the forecasted values for a given time point, instead of just a mean or a point forecast.
A quantile forecast is a forecast ...
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26 views
Feature engineering suggestion
I've to forecast the revenue generated by a company on a monthly basis. The dataset looks like this:
...
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1answer
22 views
Dummy/baseline models for time series forecasting
I am working on an evaluation of time series forecasting models in Python, more specifically with statsmodels, scikit-learn and tensorflow. I think it makes sense to first compare the model ...
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1answer
34 views
Need help with lag features in regression forecasting
I am trying to build a timeseries prediction model. The problem is that I'm still hesitant whether I should use lag features or not. What makes me wonder is the fact that the training data has these '...
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0answers
27 views
Forecasting a year ahead using annual vs daily data
Suppose, as an example, that you would like to forecast a share price in a year's time based on the past 20 years of data. You can either use annual data and forecast 1 period ahead, or use daily data ...
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0answers
25 views
Forecasting using MA(2) model when past 5 observations are known
So given an MA(2) model : Xt = Wt + Theta1 * Wt-1 + Theta2 * Wt-2
Where Wt is white noise. (Normally distributed) and Theta1 and theta2 were available.
Say if X96,X97,...X100 of the series were given ...
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2answers
425 views
Why is arima in R one time step off?
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
...
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0answers
30 views
Elastic net chooses lags beyond ACF cutoff
I've been using Elastic net for time series forecasting. I’m using first difference of the series. Normally I use the ACF to determine the number of lags to use. I was curious, if I would produce more ...
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0answers
6 views
How to handle partial observations of the variable of interest when training a time series model?
I have the following time series data:
$\{ t_i, X_i, Y_i \}$
where $i$ is the index, $t_i$ is the timestamp, $X_i$ the measured value of the external variable and $Y_{i}$ the value of the variable ...
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1answer
32 views
Linear Forecasting with a small dataset
I am trying to get some forecast (5 years more) from a small dataset that is as follows:
...
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0answers
11 views
Algorithm for producing a Moving Average (as in ARIMA) model
I have a time series $X_t$ and I want to produce an ARMA forecast (without using any automated packages - the purpose of my project is to understand how those work).
So far, I have the AR(p) part ...
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0answers
14 views
Forecasting with time series with different interval
I am trying to forecast monthly inflation rates using weekly percentage change of commodities prices. Is there a way to do this without losing info? or can I like get the predictor's moving average ...
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1answer
55 views
auto.arima throws error wrong length for 'fixed' [closed]
I am using auto.arima from the R forecast package.
When using this function with lambda parameter, it is throwing error
wrong length for 'fixed'
Here is the code ...
3
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1answer
126 views
Forecasting daily data with annual seasonality
i have been trying to do the forecasting model. My data has daily value and there is annual seasonality and probably weekly.
My question is which model will be the best. I have tried with SARiMA but i ...
1
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1answer
39 views
Forecasting with AR(1) and pseudo out-of-sample using R
I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp)
...
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3answers
314 views
Does lack of seasonality imply random time series?
Some techniques for time series analysis (prediction) require that the time series not have seasonality. It seems that without seasonality, a time series is essentially random, in which case ...
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1answer
42 views
K in Fourier series - How to find value of K to use it in ARIMA?
I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
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0answers
19 views
How to get a forecast equation for $\hat{y}$ using ETS state space model
The ets(AAA) state space model (Rob Hyndman's handbook) is as below
State equation is
\begin{equation}
Y_t = L_{t-1} + b_{t-1} + S_{t - m} + \varepsilon_t
\end{equation}
The measurement equations ...
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0answers
20 views
Dynamic regression with lagged explanatory variables
I have data on unemployment from 2006 to 2018(monthly) and have fitted a $sARIMA(3,1,1)(0,1,1)_{12}$ that has decent forecasting abilities, however I want to try to improve the forecasting abilities.
...
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0answers
14 views
Timeseries forecasting repeadted results
I'm trying to fit GBR on a timeseries of profit of a company. The code looks like this:
...
