# Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

2,505 questions
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### Volatility forecast with GARCH(1,1)

I am having trouble with this question: $Y_t = \sigma_t \epsilon_t$ $\sigma^2_t = 0.003+0.41Y^2_{t-1}+0.53 \sigma^2_{t-1}$ and I am given that $\sigma^2_T = 0.01$ and $Y_T = 0.2$. I am asked to ...
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### 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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### 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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### 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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### 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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### 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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### Which bagging & boosting algorithms can be used for time series forecasting? [on hold]

I have a dataframe as follows: ...
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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### 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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### Forecast existed ARIMA model using primer time-series

I have some fitted ARIMA model: ...
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### 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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### 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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### 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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### 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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### 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 ...