**2.406 forecasting questions.**

I have this statistics oriented question and I need some input on the approach. There is a new product introduced, a contact lence that has seleral ranges of attributes (powers) from which the users ...

I am currently working on a forecasting project and I have tried several different models to forecast with. Having trained and tuned my models I want to pick which model works best for each time ...

I've read countless posts on this site that are incredibly against the use of stepwise selection of variables using any sort of criterion whether it be p-values based, AIC, BIC, etc.
I understand why ...

we have a given time series includes a specific type of data for example from year 1980 to 2016. Also we know that we should achieve to a predefined goal(a fixed value) in year 2025. But we don't ...

I have a dataset of 2 variables that should be heavily correlated.
There are some underlying reasons why this set has an R^2 of only 0.620 when modeled in a simple Linear Regression; the independent ...

I am building a time series model to predict the zillow home prices for march 2019.I have data for each zip code from the year 1993 - 2018 and i have prices for every month.I was trying to use ARIIMA ...

I'm working on a forecasting weekly sales by category. I want to make sure I'm doing it correctly.
...

I have a time series dataset, where a customer may purchase fuel one week and not purchase again for 2-3 weeks. I need to forecast when a customer is likely to purchase and how much they will spend. ...

I am using an algortihm to generate a daily sales Forecast and have concluded that the Forecast is, for pratical purposes, of good enough quality ("low" wMAPE).
In general, and without further ...

I have a data set that includes sales dollars by sales order and I want to perform a time series forecast on it. Low dollar sales orders have very little noise and after detrending and doing some ...

I am attempting to write the mathematical model for and also simulate an MA(1) process that has drift (in R).
I have referenced ARIMA (0,1,1) or (0,1,0) - or something else?, Simulation of forecasted ...

I am working on forecasting airport delays the data looks like this
It looks like there is a structural break around 2004 where theres a huge increase and then a huge decrease around 2009.
I am ...

I am working on prediction of porduct demand. I have dataset of transactions for many customers for a whole 2017 year. Problem is that I have lot of porducts with different quantities and packages in ...

I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...

Generally, I normalize variables using standard normal variates or (x-xmin)/(xmax-xmin)But this only works well for variables that are not truncated, for example ...

I have a data set of actual scores from sporting games, matched with the bookmaker's Total Over/Under Score (O/U Score) and the odds the bookmaker was offering that the game's total score would fall ...

I have quarterly data on inflation from 1990 Quartal 1 to 2016 Quartal 3.
If I want to perform the pseudo out-of-sample forecasting one quarter ahead with an autoregressive function, do I have to ...

Need suggestions on the best technique(preferred to be incorporated in R) to predict probability % of occurrance of an event in time 't'?

General
Dear community,
I really struggle with some imporant issues for my next project. In general, the investigation is about multi-response forecasting with financial data. The predicability of ...

I'm a bit frustrated since the time series I am trying to analyse right now has definitely non-stationary curve but it's last values differ greatly from the mean making the time series stationary.
...

I am a little new to time series in general, however, I have 5 years of weekly sales data and another variable of interest.
I am trying to see if there is a trend in the sales data and the second ...

I have several intermittent data. Based on those data, I would like to compare several forecasting methods (Exponential Smoothing, Moving Average, Croston, and Syntetos-Boylan), and decide whether ...

I recently started working on a project at the University.
The main task of the project is to apply Deep Learning for forecasting. I have the dataset from a company that basically contains various ...

I have a data set which generally decreases over 24 period units. It then returns to its relatively highest state at the beginning of the period. So for instance the data may look like this:
Period 1 ...

Hi I have hourly data (one obs one hour) with multiple seasonality. I would like to fit an ARIMA model using forecast R package taking into account the multiple seasonality, maybe taking also in ...

In this post Rob Hyndman says that for forecasting, it doesn't matter whether we fit an ARMAX model or an OLS model with ARMA errors: https://robjhyndman.com/hyndsight/arimax/
Why is that the case? ...

as I am stepping into forecasting with ARIMA models, I am trying to understand how I can improve a forecast based on ARIMA fit with seasonality and drift.
My data is the following time series ( over ...

I don't understand the meaning of the above question so please can you follow up for me please answer it for me today thank

I am using the auto.arima from the forecast package in R to determine the optimal K-terms for fourier series.
After I do that, ...

I'm trying to predict a time series using a model-tree (Cubist) and I'm getting a strange behavior, I think. This is a stock market data but I'm not using the raw level of the stock price but change ...

Im just starting using R and have been getting through a number of tutorials on Forecasting as need a forecast for next year. I have been using a seasonality of 7 & 365.25 in order to take account ...

I refer to the link: http://kourentzes.com/forecasting/2016/06/17/how-to-choose-a-forecast-for-your-time-series/#comments
How should i add fitting and validation area in the plot, according to the ...

I am trying to measure the accuracy of my model in producing a multi-step forecast and I have read a lot of different opinions on the matter and am now rather confused.
The goal of my model is to ...

We know using models like ARIMA we can do out of sample predictions for a Time Series. i.e. we can know what would be the value v at time t. Can we do the reverse of it, and find at what t will be v a ...

I have a time series going from 2013 until late 2016. I am using the auto.arima function in R to forecast the next 12 months. I get the following where the black line are my observations and the blue ...

Context first, questions at the bottom.
I have 10 years of daily precipitation data that exhibits an annual seasonality, which I am trying to model using ARMA methods and then forecast. Data here, ...

I have a doubt in below ML families.
If we are predicting: Yes, then we have classification and regression
If No, then we have clustering
In clustering, we have K-means algo
In classification we ...

I forecasted/simulated a time series with a n step ahead forecast (n-ahead = 250) with 4 different time series models. Now I want to test, which of these models fits the best for the data.
All I ...

I'm completely new to forecasting so please correct me if I'm wrong.
I'm trying to forecast sales data using R. My main concern is that when I decompose the data using ...

I need your advice with regards to the following inquiry:
"Based on your observations, what could you say about the load for the same months in year 2019?"
...

I'm using the library vars in R to plot fanchart and predictions through the vector of error correction model. I have used this ...

I was under the impression that Python Statsmodels SARIMAX with seasonal order parameters set to 0 will generate the same forecasts as ARIMA. But apparently the forecasts are wildly different. What ...

I have two sets of forecasting errors, and want to perform a DM test.
Both forecasts are a fixed size moving window, and are 1 day ahead forecasts.
The first step of performing the DM test is to ...

Is there a way to implement multiple forecasting models in WEKA, where instead of one sequence of events there are multiple sequences, for different (user) identifiers?
Let's say, a traditional ...

In the context of time series, can classification be considered a sub-type of forecasting? I feel like classification is simply projecting the outcome of a certain data set onto a predefined group of ...

I have 15-minutely data (96 values per day) over several years for around 340 entities (i.e. 340 data sets or long ts).
Now my task is to forecast a 4-hour window (i.e. 16 observations) for each day ...

Multilayer perceptrons are great for discovering associations between variables defining independent events based on the same underlying associations in reality.
Less cryptically put, MLP's are great ...

I need to forecast data which has many periods of zero demand, also there is no seasonality or trend in the data.
I tried ARIMA, but it converges to the mean. I also applied some predictors, but ...

Would it make sense to overfit a model on purpose?
Say I have a use case where I know the data will not vary much respect to the training data.
I'm thinking here about traffic prediction, where the ...

- time-series
- r
- arima
- regression
- predictive-models
- machine-learning
- prediction
- seasonality
- exponential-smoothing
- neural-networks
- prediction-interval
- accuracy
- python
- autocorrelation
- garch
- cross-validation
- stationarity
- econometrics
- var
- autoregressive
- multiple-regression
- modeling
- model-selection
- self-study
- error

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