Questions tagged [forecasting]

Forecasting is the process predicting future values based on historic and current data, typically for time-series datasets.

Filter by
Sorted by
Tagged with
1
vote
0answers
25 views

What is the best model for predicting delays?

Supposing we need to predict delays based on a previous dataset that contains the history of several, lets say, providers and their delivery delays. The goal is to minimize the loss due to those ...
0
votes
0answers
8 views

Time Series Forecasting for Yearly Data

I have a project that will be focused on collecting financial data from users (Revenues and Expenses). I want to include and AI solution that can take the data for each user and give them a ...
1
vote
0answers
32 views

How do I use number of hours as index in timeseries forecasting?

I have a dataset that has number of hours (consecutive value) and total sales in that 1 hour in my dataset. See below for head of the dataset: ...
0
votes
0answers
7 views

Test data for time sequential data

If I am trying to predict: the weather, the stock market, coffee sales per city, etc. there is no good way I can see to break out the data for training vs test data. For the weather case, training ...
0
votes
0answers
9 views

How can I train a LSTM with different time series of same process?

I have multiple time series dataset of the same process (e.g: sensor collecting humidity in a manufacturing process which last 2 hours) and would like to train a LSTM model to make forecast based on ...
3
votes
0answers
58 views

Forecast Model to Estimate Customer Service Call Volume and Appropriate Staff

I am working on a project to predict the proper staffing needed for a customer service team using historical data. I am new to machine learning, and I am not sure if my approach to this problem is the ...
1
vote
0answers
11 views

How to compare different forecasting models over different time horizon?

Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons. The time series data is ...
0
votes
0answers
17 views

Time series Forecasting without consistent timestamps

I am currently working on a time series forecasting model with a dataset that does not have consistent timestamps i.e. one row every 60 seconds. Is it possible to train an accurate model with this ...
0
votes
1answer
13 views

What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
0
votes
0answers
188 views

Grouped Time Series forecasting with scikit-hts

I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
0
votes
0answers
21 views

How to use time series forecasts as input features?

I have a time series dataset containing daily data like below. Let's assume that I would like to make some forecasts of my temporal serie (x) and use it as a second feature feature (f) to predict the ...
0
votes
1answer
18 views

What are more advanced techniques than ARIMA?

For timeseries predication cases, what other techniques are available in statistics or machine/deep learning other than MA (moving average), ARMA, and ARIMA?
3
votes
1answer
32 views

What reccent alternatives to LSTM are there for regression problems?

I have been working for a while on a regression problem - predicting the air pollution in a city based on meteorological features (humidity, temperature, wind velocity a.o.). I have trained an LSTM ...
0
votes
1answer
28 views

Scaling multi input LSTM

I have a single layer LSTM model with 300 time series which try to predict the next value for one time series, based on past 12 values of the 300 time series. 56 is the number of slices of length 12 ...
0
votes
0answers
7 views

How to manage multiple timeseries model for large number of users?

I have to build timeseries forecasting for large number of users around 50K, what will be the ideal strategy to build forecasting model for such scenario where there are so many different users ?
1
vote
1answer
53 views

Managing Multiple Observation at the same time stamp timeseries forecasting deep learning

I have a dataset timeseries forecasting that includes the categorical columns and numeric as well. here is a sample of it ...
0
votes
1answer
80 views

Very low error during training of a RNN for forecasting but high test error

I use a Recurrent Neural Network for time series forecasting of electrical load data from a cooling device based on past values of the load time series and temperature values. I first normalize the ...
0
votes
1answer
29 views

ML algorithm for high dimensional time series forecasting

I'm trying to make a forecasting model for goods prices in an economy (trying to forecast inflation). Dataset: has 300 goods prices % monthly variations for last 6 years. And also added $n$ ...
0
votes
0answers
18 views

How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
0
votes
0answers
111 views

Var_Imp Algorithms in Pred/Class Problems: Can I use it in TS Problems?

OBJECTIVE OF THIS POST: Solve a query about the possibility of use prediction/classification variable importance tools in a time series type dataframe. Collect the largest number of variable ...
0
votes
0answers
10 views

Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...
0
votes
0answers
14 views

identifying time series with threshold breach potential

(moved from stackoverflow.com) Hi all, I'm trying to solve a following problem. I have a set of various devices feeding their readings into a system where they are stored as time series: timestamp, ...
0
votes
0answers
13 views

Forecast methodology for geographic variables that are somewhat related

I'm creating time series forecasts for different geographies and wanted an expert opinion on how I can take into account geographic relationship to improve my model. Is there an algorithm that's ...
0
votes
0answers
12 views

What could be reasons for higher MAPE?

I built two models on a dataset where data for independent variables (X) being the same and dependent variable (Y) changes for each model eg : Y is price value for a particular region. Y values change ...
0
votes
0answers
30 views

Getting KeyError while executing forcast( ) in statsmodel's holtwinters function

I'm trying to get time series prediction using the following code. ...
1
vote
1answer
13 views

How to deal with mismatch of timesteps in target variable and features in forecasting problem?

Background Info: I am working with some climate data where I want to predict crop yields with my dataset containing climate- and satellite-derived features. This is a time series regression ...
1
vote
0answers
20 views

Sarimax forecast : How to properly deal with non working days

(asked first on stackoverflow but felt like it would be smarter to put it here) I'm trying to build a Sarima model to predict day by day the expected value of several measures (separately), the point ...
1
vote
0answers
40 views

Panel data classification and regression?

I am very new to time series/panel/longituginal data. From my understanding Panel data = multi-object time series and I have some panel data in long format (objects have multiple rows corresponding to ...
1
vote
0answers
18 views

Choosing kernel size of cnn for time series data with multiple seasonalities

I try to solve a standard time series forecasting problem using convolutional neural networks. The data has multiple seasonalities and so I wonder if a kernel size should reflect this fact e.g. for a ...
0
votes
0answers
19 views

Optimise for the sum of regression predictions?

I'm building a machine learning model to forecast the number of students on a course at a University. I'm currently optimising for MAE for each sample (i.e. a ...
1
vote
0answers
23 views

CoxPH model with Frailty and L1 regularization

This question stems from an approach proposed by Dr. Silverman, "Predicting Horse Race winners through A Regularized Conditional Logistic Regression with Frailty." In this paper, he proposes ...
0
votes
0answers
9 views

Is it possible to pass multiple features at once to Croston method?

I want to implement the Croston method for intermittent demand. I have a data frame that has 10 features and all those have many zeros in them. I want to pass the entire data frame to the Croston ...
2
votes
0answers
31 views

Time series forecasting for vibration prediction on Industrial machine production?

I'm working on a machine learning project related to an industrial machine. The goal of the project is to build a model that would be able to predict the vibration of the machine while it's in ...
0
votes
0answers
32 views

Predicting Y Values Properly in a Regression Task using Scaled Values (Random Forest & MLP)

I have a supervised learning regression task: I am trying to forecast demand for a product based on sales in past years. Data description: Samples (rows) - Demand for a certain product (at a certain ...
0
votes
0answers
96 views

Time series forecasting in Python with 2 categorical variables

What approach is the best for a time series forecasting where you want to include 2 categorical variables in python? Im not finding any useful information that can help guide me with this; mainly ...
0
votes
0answers
9 views

Forecasting mon missing time timeseries data

I have time series data for minutes interval. But due to some noise i have to remove some rows from data. Now, I have data with some missing time stamp. What should i do for forecasting in this case?
0
votes
0answers
12 views

In Time Series forecast, should Scaling be done on both train and test features combined ( test is 1 new data point)?

Let say I have a Time series, I'm using sliding/expanding window method to split to train and test data: train would be all the data I have until day x and test is day x+1. To avoid Data leakage I'm ...
1
vote
0answers
57 views

Machine Learning Model for Time Series Forecasting

I am using Random Forest, SVM, and XGBoost models to nowcast/forecast an economic time series variable. However, I would like to extend these models to optimize/customize them for time series ...
1
vote
0answers
35 views

One Year Ahead Forecasting with Unevenly Spaced Time Series

I have many products in my warehouses which can be "demanded" any day by my different clients. I want to forecast how many of each item will be demanded for the whole next year. Naturally, ...
0
votes
0answers
6 views

Time series forecasting for stable pattern with some sudden changes

In my case, the time series is around a constant value with very small fluctuations. But, sometimes the signal starts increasing or decreasing for some duration of time. For my application, such ...
0
votes
0answers
9 views

Time series forecasting when one of the series is known

I have a problem where there are two time series $\{x_t\}_{t \geq 1}$ and $\{z_t\}_{t \geq 1}$. These two time series are correlated for fixed time instant but uncorrelated with each other across time....
0
votes
1answer
276 views

Predicting out-of-sample time points with LSTM

I'm working on a time series forecasting problem using LSTM. The data is univariate and non-stationary. I followed this tutorial. The data is processed as the following: First, the difference between ...
0
votes
1answer
31 views

Forecasting with Neural network and understanding which underlying model is favoured

If I have a very large set of data (~ 1TB). How can I use Neural Network on this data to understand which underlying distribution (eg. let's say a Gaussian or a Poissonian with a certain mean, sd) is ...
0
votes
0answers
23 views

Multivariate time series forecasting with the extended Theta method

I am looking for an implementation (better if in Python) of the extended Theta method for multivariate time series forecasting, presented in the following paper: D. Thomakos, K. Nikolopoulos, ...
4
votes
3answers
1k views

Is time series forecasting possible with a transformer?

For my bachelor project I've been tasked with making a transformer that can forecast time series data, specifically powergrid data. I need to take a univariate time series of length N, that can then ...
1
vote
0answers
9 views
0
votes
0answers
24 views

Neural net performance using rmse

I am trying to build a NN which can predict exchange values. I am quite new to R and NN and I don't quite understand how I could improve the performance metrics of the neural network. I have tried ...
1
vote
1answer
108 views

Predicting high frequency sparse time series data in python

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0's, since there is no continuous flow of cars coming to charge but rather ...
2
votes
1answer
40 views

Predicting sparse time series data

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0s, since there is no continuous flow of cars coming to charge, but rather ...

1
2 3 4 5 6