Questions tagged [forecasting]
Prediction of the future events. It is a special case of [prediction], in the context of [time-series].
3,204
questions
0
votes
0answers
21 views
Rolling Time Series Forecasting with data on underperforming year such as COVID scenario i.e. 2020
Recently I am working on a project where I am to forecast volume and sales of merchandise as well as fuel.
While streamlining my thoughts and the method to go ahead with the exercise I realized that ...
1
vote
3answers
27 views
If the autocorrelation below confidence for time series
If the autocorrelation below confidence for time series, does it mean, that there is no sense to you past (lagged) data for the forecasting?
And probably the best result is a distribution mean or ...
0
votes
1answer
22 views
Is it possible to calculate the prediction intervals for top down, bottom up, and middle out reconciliation of hierarchical time series?
I have read in several places (heres one) that we can not calculate prediction intervals for the classical reconciliation approaches, top down, middle out, and bottom up, and hence optimal ...
0
votes
0answers
12 views
Recalibration of constants with ordered logit for forecasting
I have estimated an order logit model: I have one utility function (without constant, in the form $\sum_i \beta_i * x_i$) with cuts and I estimated the $\beta_i$ and the cuts on revealed preference ...
2
votes
1answer
33 views
ARIMA (auto.arima in R) forecasts 0 for high variance and large time series
I'm noticing an issue with using auto.arima in R where if I input a series with large values and high variance, the forecast simply returns 0. Is this because the ...
0
votes
1answer
30 views
How to expand weekly time series data from 38 weeks to 52 weeks format?
Recently I have been working with weekly data that has in total 52 weeks. Later I received data with an external variable and it is also in weekly format but the whole year has in total 38 weeks. The ...
1
vote
0answers
19 views
Which data do I have to use for forecasting? The transformed one or the real one?
I have a data about museum visitor in 2011-2019. I divide this data into training set (2011-2018) and testing set (2019). The training set is nonstationary so I used Box-Cox transformation and ...
0
votes
0answers
19 views
What if my seasonal decompose is completely coloured?
I have a dataset for solar output, at day the values are positive but at night the values are 0. When i try the seasonal_decompose() function It gives me the following result:
Im pretty sure the ...
1
vote
1answer
29 views
Independence of consecutive data points in a Seasonal ARIMA model with all zero non-seasonal variables?
I have built a SARIMA model for forecasting monthly income data in python using the pmdarima library. It gives a confidence interval for each monthly prediction. I want to combine the data til the end ...
0
votes
1answer
30 views
Survival analysis - different start times
I am doing a survival analysis on vending machines based on their time to failure.
However, my machines are deployed in 4 main locations, machines are all installed at a different time depending on ...
1
vote
1answer
60 views
How to deal with extensive outlier periods caused by external factors like COVID?
I'm working on a timeseries that was significantly affected by the COVID-19 pandemic, but has since recovered to a more normal behavior. My objective is to have a good forecast for a couple of weeks, ...
1
vote
0answers
26 views
How to interpret the coefficients of a regressor in a ARIMAX model
I'm fitting an ARIMAX model with statsmodels SARIMAX library. I've done a logarithm transformation for both the dependent variable and the regressor and I've been left with the following coefficients:
...
0
votes
0answers
14 views
Serial autocorrelation in random forest with daily climate data
I have a daily measurement of soil moisture at 2,000 locations across North America for 3 years. Soil moisture and climate data are highly correlated, so I want to build a RF model that predicts daily ...
7
votes
1answer
233 views
What is use of Tweedie or poisson loss/objective function in XGboost and Deep learning models
I am looking at few competitions in kaggle where people used tweedie loss or poisson loss as objective function for forecasting sales or predicting insurance claims.
Can someone please explain the ...
2
votes
1answer
37 views
Which algorithm can I use for breaking down monthly forecast into daily forecasts / buckets?
I'm a trainee at a medical device distribution center. My internship project is to break down monthly forecast into daily forecasts / buckets. in the current situation the monthly forecast is broken ...
1
vote
0answers
23 views
Survival analysis with different size of failures
I have a dataset which includes both time to failure events as well as size/cost of failure. I am interested in estimating (preferably non-parametrically) the cumulative cost of failures as a function ...
9
votes
3answers
318 views
Accuracy of Volatility Forecast
I understand the basic concept of ARCH/GARCH models and the basic mathics behind it. That is, one models the "volatility" of a time series, i.e. the residuals of a time series describing ...
0
votes
0answers
9 views
Predict time series of parishable and seasonal product just with 1 year dataset
I want to predict the amount of demand for several types of fruit in a number of market with LSTM model.$ $
But I have a big problem, that I only have the dataset of one last year and because of that ...
0
votes
0answers
14 views
How to make sense of rescaling time series of counts?
I'd like to forecast time series of counts : sold items. Each time series represents monthly sales.
I also believe that there are clusters within the series, with low, medium and high count items. ...
3
votes
0answers
53 views
Predicting the Residuals of a Forecast Model
I have just read a paper [1] in which the authors try to forecast risk of some variable (earnings in this case) by deriving dispersion measures via forecasting quantiles of the respective variable, i....
0
votes
0answers
22 views
Time-series prediction shifted from the actual
I am trying to predict the AAPL stock price 5-days out from today's closed. I have included technical features like 20, 50 and 200 days moving averages in the price ...
0
votes
0answers
8 views
Forecasts combination via weights based on normal distribution
I am working on combining forecasts. I thought of calculating the weights based on normal distribution. This latter is fitted on the past values of the time series.
My issue is, should the weight be ...
2
votes
1answer
88 views
How can I calculate the parameters of a MA time series model?
I am new to Time Series Analysis and I have problems understanding the MA-model (opposed to the AR model). I read many webpages about it and it is either said that MA is a linear regression with past ...
0
votes
1answer
56 views
ARIMA model with delay in fitting and constant prediction
I am trying to use ARIMA (Python, statsmodel) on the following time series, values are collected with a weekly frequency:
...
0
votes
1answer
46 views
How does forecast::tsclean() detect outliers in R?
Does it use a particular z-score?
I know that it does apply STL.
My data is seasonal, and had quite a few outliers, so I am just wondering how exactly it determined whether a particular data point is ...
0
votes
0answers
26 views
Can I hybridize between ARIMA model and exponential smoothing?
I have a time series that stabilized at the first difference (d=1) and the model was ARIMA(0.1.0), as I know it is a model that does not really predict.
In this case I relied on hybridization as ...
2
votes
1answer
61 views
Time series analysis of daily temperature data in R
I am pretty new to the topic of time series analysis and I am trying to use the package "forecast" on daily temperature data to predict the daily temperature in the future. To be precise, I ...
1
vote
1answer
20 views
Bayesian methods for multi-day time series prediction
I have been looking at recurrent neural networks and LSTM models for time series, and it is interesting that they can predict multiple days ahead. For example, I can take inputs of 100 days and ...
0
votes
0answers
39 views
Forecasting the spikes in time series river gage data
I have an idea for a personal project knitting together river level gage data with weather data sets to look at how upstream and surrounding area rainfall events affect river levels. I would probably ...
0
votes
0answers
35 views
Forecasting Quarterly Time Series Data?
I've gotten very confused reading all the articles about forecasting time series data with seasonality on Medium and other sources. It seems that many provide useful background and importance of ...
5
votes
1answer
150 views
ARIMA Model Changing With New Data
I developed an ARIMA model with errors (https://robjhyndman.com/hyndsight/arimax/) to forecast the GDP growth of a small region. The issue is that the GDP values of past quarters and years change ...
1
vote
1answer
31 views
Which tree ensemble algorithms are the most suitable for time series forecasting (regression)?
Decision tree ensemble models are very practical for building predictive ML models. They are not strict on assumptions, can work on data without too much preprocessing, train fast and typically result ...
0
votes
0answers
26 views
What ARIMA and UCM model would you recommend for this monthly time series?
I have 4 years of historical data which is monthly and has a strong seasonality. I've been struggling to figure out what are the correct model specifications for both ARIMA and UCM. I also cannot ...
0
votes
0answers
87 views
Why should we remove trend and seasonality before forecasting?
Why should we remove trend and seasonality (hence, making a series stationary) before forecasting? If time series has a particular trend/seasonality, shouldn't we incorporate that model instead ...
0
votes
0answers
85 views
Running auto.arima with exogenous variables
I have weekly sales data over many years and my data shows clear seasonality + few other well defined spikes. For instance, there are always spikes around major holidays like Christmas and ...
0
votes
3answers
94 views
Modelling the logarithm of a response
My response variable is positive and I decided to model the logarithm of the response.
Some of the values are zero. For this reason I modelled $Z = \log(Y + 0.1)$. When I transform back, some of my ...
0
votes
0answers
27 views
Stationary Time series is showing better results when predicting(ARIMA) after differencing
I have a time series of daily maximum temperature of a city for 2 years 3 months. I removed the seasonality from the data by subtracting present values with the past year values(seasonal differencing)....
0
votes
0answers
64 views
Does k-fold cross-validation induce data leakage in time series data?
I created a predicative model using neural networks and applied in on a time series. This is how I split my data:
...
0
votes
1answer
39 views
SNAIVE METHOD getting same result for different estimators (BU, OLS and STRUC) on THIEF package in R (TSAGGREGATES + RECONCILETHIEF)
I'm stucked and don't know how to solve that problem using thief package in R (actually tsaggragates + reconcile thief) for SNAIVE METHOD. Does anyone can check it and see what is wrong with it? If it'...
0
votes
0answers
26 views
Smoothing train/test data
I am currently working on time series forecasting. I know that the first step is to divide the time series into train and test. Then I also understand that I have to normalize the test set using the ...
0
votes
0answers
17 views
Forecasting surface visibility with (right-)censored data
I have a bunch of surface visibility data measured at several ground weather stations over a certain period of time. The data for some of the stations are right-censored, e.g. a significant percentage ...
0
votes
0answers
35 views
Time series forecast with multiple time series
Supposing that I have 1000 data of characteristics of a machine (Period = 1..1000), with 6 dependent variables (Pressure, Speed, Temperature, Sound, Noise, and Vibration). Most of these variables ...
0
votes
1answer
29 views
How to graph ARIMA forecasts vs past values in R
I have performed an ARIMA model and I am happy with my results. Now I am trying to show that my forecast is good. Is it possible to create a graph that shows the forecast vs past values?
What I am ...
1
vote
1answer
25 views
Forecasting two part ARIMA-GARCH model
I am conducting the ARIMA-GARCH model in two stages. First, I assess the ARIMA model and then apply GARCH model on the residuals from the ARIMA model. My model looks like this:
...
0
votes
1answer
49 views
Time series forecast where each measurment is already averaged and has a spread
I would like to forecast a time series consisting of time averaged (everything happening during 15min intervals is averaged and recorded with a timestamp of the start of messurment) quantities (...
0
votes
1answer
18 views
Cox PH Tenure Handling
I'm working on creating a survival model to forecast customer retention using the Cox Proportional Hazard model in R. I'm using the tenure of existing customers, in addition to other metrics, such as ...
1
vote
1answer
49 views
Forecasting aircraft flights per month
I'm trying to solve a time series forecasting problem, specifically I want to use historical data with an aspect of COVID-19 impact on global and regional aviation, number of new COVID-19 cases per ...
1
vote
0answers
77 views
Prediction intervals from Linear regression and Arima for DYNAMIC forecasting
I am comparing prediction intervals from linear regression and ARIMA for a simple AR(1) model:
p = lag(p)
The models were built on monthly data from 2003-2013 years. Predictions were made for 2014 ...
0
votes
0answers
7 views
ARIMA models: maximum reasonable horizon year, validation, model sensibility if extra year is added to sample
I am modelling a yearly univariate time series with 50 observations (1970 – 2019). I am interested in fitting an ARIMA model for forecasting purposes (in R). I have the following questions:
What is ...
1
vote
1answer
139 views
Time Series Forecasting: ARIMA\VARIMA vs Machine Learning \ Deep Learning
I am working on the development of a time series forecasting, and I have some doubts on the model I should use to achieve better results.
PREMISE:
Multivariate Time Series: my time series is a ...
