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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Comparison of MAE and Mean to illustrate the error magnitude
I have predicted a time series with positive, zero and negative values. As error measurement I used the Mean Absolute Error (MAE).
In order to give the reader of my paper a better understanding ...
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35 views
Prediction of $X_{n+1}$ with Yule-Walker estimate
Consider a causal AR(1) process $X_t = \phi X_{t−1} + Z_t$ with $(Z_t)$ iid with mean 0 and finite variance. I am reading in a book, that $\phi X_n$ is the best predictor for $X_{n+1}$ because it ...
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machine breakdown forecasting
I have 2 .xls worksheets. The first one contains columns with historical machine sensors data. The second page contains machine interruption. I should predict machine breakdown. Could you please ...
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1answer
27 views
Prewhitening with seasonal response and non-seasonal independent variable
I'm working to develop a forecasting model for a quarterly seasonal variable (quarterly estimated individual income tax payments) using several candidates for non-seasonal independent variables (...
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8 views
how to estimate hyper-parameters when cross-validating time series forecasting?
I want to evaluate several forecasting methods on the taylor time series using cross-validation.
How do I go about selecting the hyper-parameters for the methods? ...
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12 views
Is it necessarily incorrect to randomize train-test split for a time series random forest model?
As part of some preliminary research, I'm experimenting with a random forest classification model for predicting whether the S&P 500 will be higher or lower at tomorrow's close versus today's ...
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0answers
24 views
How does BATS model work?
I am using BATS on a univariate time series model. I have observed strange behaviour. I have data from 2016 to till date (weekly level). If actual are considered from 2016 to 2019 May, I have used ...
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7 views
RNN (LSTM) training on multiple time series
Regarding RNN training, We feed network a network -> point by point from the same time series (or image or smth else).
When we “switch from one time series to another”, what should be done or how ...
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1answer
18 views
Expected value of an AR(1)
Consider the AR(1) model
y_t=α+βy_(t-1)+e_t
Where the errors are white noise N(0, 1).
What is the expected value of y_t.
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1answer
39 views
Is this time series stationary? What would be your approach to forecasting it? [on hold]
I've been working on the time series prediction of a signal and came across a small misunderstanding. The signal is depicted below:
Apparently it looks like there are several stationary local areas ...
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0answers
29 views
Is it possible to bootstrap a Diebold-Mariano test?
I am currently working on a small project where I want to use a (two-sided) 1-step ahead ($h=1$) Diebold Mariano test to compare forecast losses for different realized measures calculated on time-...
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1answer
23 views
why isn't the tscv function allowing for step-size other than 1?
the tsCV function from the forecast package allows for forecasting using a rolling forecast window. Why is it that the step-size is always 1 sample ahead, and the ...
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How can I accomodate the forecast series to separately fit and predict workdays and weekends?
I'm using the forecast R package. I have the same dilemma as How to forecast daily time series with weekends and holidays?. I understand that the answers consensus ...
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10 views
extracting seasonality - using fourier transform vs. learning the coefficients of fourier terms
I'm following hyndman's advice for using fourier terms when fitting a linear regression model to the taylor time-series (with the very long seasonality of 336). My ...
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0answers
6 views
Neural Networks and a catalogue of the number of floating point operations in various types of statistical and time series models
I recently came across this paper Green AI.
In the paper they discussed using floating point operation (FPO) count during testing and implementation of Neural Networks (NNs) to compare the ...
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17 views
Forecasting based on previous realizations
A given process has multiple variables moving to a target point. I can collect data of several realizations of this process. What forecasts methods exist for this? I'm familiar with exponential ...
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48 views
Short term(10 mins period) forecast using ARIMA
I have 14 months(01/07/2018 to 30/08/2019) of one minute data, which I have aggregated to 10 mins block. So I have a data of dimension "61056 * 350". From this I am using 12 months of data to train ...
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69 views
Does smoothed data work better for time series forecasting with LSTMs?
I am training a 3-layer LSTM on time series data ($10^6$ training samples) to predict the next point in the time series, where there is no seasonality and
the time series has been made stationary (...
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0answers
25 views
Is it possible to reconcile forecasts that are not only at different levels of an organizational hierarchy, but also measured in different units?
Consider the following scenario:
I have a top line revenue forecasts in dollars for each of my corporate divisions or business units.
I have capacity forecasts for each of my regional manufacturing ...
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8 views
Uncertainty of additively combined models
I'm dealing with sales forecasts (time-series with few data points) for a company on a department level (one company consists out of multiple departments). My approach is it to build a time-series ...
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1answer
23 views
tsCV auto.arima with xreg results in NAs
I'm using the tsCV function from the forecast package in R. I'm comparing arima with and without xreg. Without xreg I have no problems. But when I add xreg to the tsCV function I get a matrix of NAs.
...
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17 views
Sparse time series of non negative monthly defect percentage [closed]
I want to forecast monthly (5th business day of month, no timestamp) defect percentages for multiple states of USA. Data is sparse (with many zeros, not geospatial) and could be any percentage between ...
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4answers
70 views
How to predict how the time series behaves in the future?
I was wondering how a statistician estimates how a time series continues. Namely, which is more accurate way to estimate the series?
Draw a line between start and end points of the time series data ...
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2answers
31 views
Predictive Modelling to predict next period sales
I have a Customer data (20 periods) which contains Sales of each customer by Period P1,P2,P3 ... P20) (each period =14 days)
Is there anyway to predict P21 period ?
Can I used ARIMA or ETS keeping ...
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25 views
Forecasting for Regression with sARIMA errors only needing new xreg's
I would like to get a better sense of why we only need new exogenous regressor data for forecasting Regression with sARIMA errors to new time periods, and not new time series data.
Specifically what ...
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18 views
Narrowing down model for 2-independent variable time series forecasting
I've got a project I'd like to start working on and it roughly goes like this: I have past time series data for thousands of products that a company produces. There is a high weekly and yearly ...
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20 views
How to fit a regression model with ARIMA errors on the seasonally adjusted component of a time series (in R)?
I want to do these two things (combined) with a time series T:
forecast the seasonally adjusted component of T (STL used for the decomposition) and "add back" the seasonality (I assume that the ...
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20 views
ML preprocess to achieve stationarity
I would like to use Machine learning models on top of multivariate time series data to forecast long horizons (for example 400 items and their historical sales in the last year & content features)...
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1answer
42 views
Is it possible to forecast multivariate time series using exponential smoothing equations? If yes what are those equations?
I know we can forecast univariate time series using different models of exponential smoothing , but am searching for whether same can be extended to multivariate time series and if yes what are those ...
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25 views
Capturing cycles in time series with Fourier
In time series regression with Fourier, Fourier terms are limited to maximum half of the frequency of time series to capture seasonality.
my question is: Can we extend Fourier terms beyond half of ...
2
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1answer
27 views
time series forecasting - predicting the next 24 hours
I have much the same problem as predict-the-next-24-hours, I have several years of hourly data of demand, and I would like to predict the next 24 hours.
Ignoring the multi-seasonality issues - is it ...
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1answer
23 views
WMAPE / WAPE for the evaluation of time series with positive and negative values
I have a time series y that has both positive and negative that I want to predict. For the prediction I normalize the values to a range between 0 and 1.
If I give ...
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26 views
Prediction interval for multi step forecast
If I have a multi-step forecast of some timeseries, where the model is some auto-regressive function
y[t] = f(y[t-1],x1[t],x2[t])
is it a valide approach to use sections of historical data to train ...
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0answers
35 views
Adjustment of the forecast of a time series for the analysis of a system
I have a simulation model of a system which receives a forecast of a time series as input. In my scientific work I would like to examine how the performance of the simulation model behaves in relation ...
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0answers
39 views
Demand forecasting
I am forecasting number of phone calls (y) i ll be getting based on the products sold(x) and I am doing the following:
(forecasted y) = (y/x averaged for past three weeks) * (forecasted x).
1. Please ...
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20 views
weekly forecasting
I am building a weekly forecasting model using dynamic harmonic regression model with Arima errors and Fourier terms. My understanding is that Fourier terms take care of seasonality of any length ...
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23 views
Improving accuracy in time series forecasting [duplicate]
I am trying to forecast a very typical sales data. I have tried Arima, ETS, Holtwinters and even neural networks but I can't get a model with more than 40% accuracy [Absolute sum of(forecast-Actual)/...
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1answer
75 views
book recommendation: statistics (time-series) for small samples [closed]
I was hoping someone could recommend a good book on statistical methods for time-series on small data samples. We are really looking to do some forecasting/extrapolation given 13 years of previous ...
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32 views
What is a Good Error Target
I am modeling a forecast for product categories using auto Arima in R. I'm getting MAPE of between 9-15% on average. We don't have any historical records of forecasts vs actual so I don't know how ...
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19 views
What forecast method is correct to apply to data in range -1 to 1
I have metrological data they strictly have range -1 to 1. For example
-0,97654
0,0376544
0,049765976
-0,578
measurement values are tied to a point in time ....
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1answer
80 views
Should I avoid mixed ARMA models?
I have hourly demand data for taxi rides that spans several years into the past. I want to use it in order to forecast future demand (for the next day). Robert Nau warns against the usage of a mixed ...
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39 views
Any ideas on how to forecast this timeseries?
I have the following timeseries and unfortunately no other information except time and holidays.
What methods could work for a probabilistic forecast of this?
I thought about some regime changing ...
2
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1answer
21 views
Why forecast::accuracy() mape is working with 0/0?
I'm learning now some metrics of goodness about time series forecasting, using the forecast package, but I'm stuck in something that surely I've not understood well....
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1answer
24 views
auto.arima with an input of another series
Two time series, which are related to each other.
I want to see if "order" helps in predicting sales, by running auto.arima ...
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0answers
16 views
What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable? [closed]
I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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1answer
26 views
checking accuracy of a forecast [closed]
I am trying to predict future sale of a product by using holt-winters
imp info:
nextsalesf contains the forecast for next 5 periods
val$Qty.2011001FBL0010250[5] is the known values of next 5 ...
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13 views
Choosing the correct moving average model
I am working with the in built Air Passenger data set in R to learn forecasting.
After splitting the data in 120:24 data points, I am trying to extract trend ...
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0answers
34 views
How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?
I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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42 views
Product mix forecasting method
I have a main segment which includes different products. I have the percentages for each product and two year quarterly data. By using this information I want to forecast next years' percentages. I ...
