Questions tagged [forecasting]

The tag has no usage guidance.

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

Demand forecasting with marketing budget data

I'm trying to build a demand forecasting model to predict future daily orders of an online food takeout service (similar to UberEats or DoorDash). My first model uses a univariate approach, which is ...
1
vote
1answer
26 views

Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
0
votes
0answers
18 views

Can multiple time series take advantage of each other?

I want to forecast house prices market in multiple cities of the same country. I expect demography, interest rate and neighbors cities values to have an impact on my prediction. For every city, I have ...
0
votes
1answer
16 views

How to predict churn events that may happen within a period of time?

I am trying to build a model that predicts churn events in the future. The business wants to be able to identify which customers are likely to terminate the services within a month. "Within a ...
0
votes
0answers
12 views

useful method for analyzing the real-time (online learning) time series? forecasting and decomposition?

Which method is useful for analyzing the real-time time series? even for forecasting or decomposition? I have a large dataset (sequential data) that need for predication analysis and decomposition ...
0
votes
1answer
34 views

Data Cleaning Techniques - What to drop?

I have been struggling with this problem for a few weeks but it seems to be out of my league. I have a dataset of product sales over the course of 55 weeks. It contains information on the store ID, ...
0
votes
1answer
17 views

How to detect time for the future events in time series data?

I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence ...
1
vote
0answers
14 views

Time series forecasting with constraints

I want to predict the passenger flow volume of an airline route, which subjects to supply capacity constraints of the route (i.e., the passenger flow volume should not be higher than the supply ...
0
votes
0answers
15 views

How far into the future can I forecast a time-series with an LSTM and strongly seasonal data

I am working on a Sequence-to-Sequence + Attention model for some time-series data. Now I have a really long time series, basically 40 years of daily observations for multiple sensors. The data itself ...
0
votes
0answers
8 views

Forecasting monthly visitor count from daily values

I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be. Depending on the visitor count on a day of the month, I ...
1
vote
0answers
28 views

Machine Learning algorithms and Panel data

I have a large panel dataset composed of $N$ stocks, $T$ quarterly dates and $K$ features for each stock. The dataset looks like the following: ...
0
votes
0answers
22 views

How to account for rare events at different time intervals while using LSTM neural networks?

I'm working on an interesting sequence-to-sequence (regression) time series problem where some static features/rare events can change the behavior of future time series. The problem is a forecasting ...
1
vote
1answer
198 views

Difference between sequence length and batch size in time series forecasting

I am using Keras for time series forecasting and I am trying to understand the tutorial on the offical site of keras about time series forecasting that you can find here (https://keras.io/examples/...
0
votes
1answer
81 views

Azure automl time series forecasting error

I'm using Microsoft Azure automl to try and generate models for time series forecasting but I keep getting an error: ...
0
votes
0answers
11 views

Forecasting: Regression - Worse performance for a subset of data

I have a dataset for which I'm using different forecasting methods (Ridge/Lasso Regression / Random Forests / AdaBoost / Gradient Boosting) to compare their performance. For the full dataset, I ...
0
votes
0answers
11 views

Demand Forecasting - Error in predictions

I've been trying to do daily demand forecasting using the H2O AutoML package for using 4 years of daily sales data as the training set. However, the daily estimates always seems to find a range around ...
-1
votes
1answer
16 views

Forecasting: Multiple Linear Regression (OLS) outperforms Random Forests / Gradient Boosting / AdaBoost

I'm using different forecasting methods on a dataset to try and compare the accuracy of these methods. For some reason, multiple linear regression (OLS) is outperforming RF, GB and AdaBoost when ...
1
vote
1answer
23 views

Long range forecasting with sequence-to-sequence models

I have a task where I want to forecast daily observations for 1 year or 2 years in advance at multiple locations--so 365 or 730 days in advance. I actually have a pretty good dataset, meaning daily ...
0
votes
1answer
23 views

LSTM model bad forecasts

I tried to implement LSTM model for time-series prediction. Below is my trial code. This code runs without error. ...
0
votes
0answers
19 views

Forecasting with two separate periods of time

I have a sales forecasting problem where I only have three months of year 2018 ( Jan, fev, march) and three months of year 2019 (oct, nov,dec) and I have to forecast the sales for all the months of ...
1
vote
2answers
40 views

Predicting Multiple Values Values Using Time Series Forecasting

I want to illustrate my question with the following example: I have a wholesale company through which I sell 200 products: P1,P2,P3 .... P200 to a 1000 customers ...
2
votes
1answer
114 views

Why does the smallest LSTM I can make perform so well on this time series forecast?

So I've been playing with some different forecasting methods on a data set that I have done some more basic analyses for in the past. Without going into to much detail, it's population data over time ...
1
vote
0answers
17 views

Impact of covid19 in forecasting models

I have sales training data from 2019-06 to 2020-06 and I have to predict sales from 2020-06 ...
0
votes
0answers
65 views

Time series forecasting - Multiple Parallel Input and Multi-step Output

I have a time series with 5 variables, and I want to predict the behavior of these 5 variables 112 periods ahead. For this, I use a dataset with information on the 21504 periods before (18278 periods ...
1
vote
2answers
69 views

how do I predict the next's alarms ? (time series) [closed]

I'm trying to solve a time series forecasting problem, where the main goal is to read data with various alarm logs and make a prediction about what may happen in the future. Specifically, my data is a ...
-1
votes
1answer
40 views

Algorithm for Multivariable timeseries prediction (COVID forecast)

I am trying to forecast tomorrow's COVID-19 cases in my country. I tried a simple Linear Regression implementation based on the "new_positives" field but it does not work very well. I had ...
1
vote
1answer
45 views

Time series analysis model evaluation performance metrics integration in time series application

After study in time series analysis, I recognized RMSE and MAPE are the best evaluation metrics for used model in real time series application. But my queries are below as this is my first practice ...
1
vote
0answers
24 views

How to model a conditional demand forecast model as ex-ante forecast for a moving population?

Goal: I am trying to forecast demand from a specific population given a specific promotion on a certain period of time frame. The data is of the format: date | promotion % | sales in 1000s(y) 01-Jan |...
1
vote
0answers
28 views

How to handle large systematic missing data in time series?

I have this time series, where on the weekends, the dependent variable values are missing. It's only a time series, I do not have any exogenous regressors/features. The dependent variable value is an ...
0
votes
2answers
37 views

Time series forecast for small data set

I am new in data science so please accept my apology in advance if my question sounds stupid. I want to do a time series forecast of outage mins in the current regulatory year. The regulatory year ...
1
vote
0answers
34 views

Getting vague results using VAR time series forecasting in python!

Firstly, I am a beginner in this field of Data Science and have tried to implement some time series models for wind speed forecasting. Also, I am aware of the fact that some regression models might ...
0
votes
0answers
68 views

Forecasting revenue/ROI based on different advertising spend scenarios

Example: Let’s say a company spends 1,000,000 USD a year on online advertising and the sales directly attributed (via tracking, etc) to that spend is 2,000,000 USD (100% ROI). How can that company ...
0
votes
0answers
19 views

Implemented AR to predict a time series, get nonsense out

I have this time series, it's an industry metric, the data is reported daily, and the data spans just over 1 year. Sometimes, the data does not get reported, and for that day, we would just use the ...
1
vote
1answer
17 views

How to predict orders with a range of items? And total orders which sum up to the total?

So I do have data like this: With the help of distinct order IDs, I can figure out how many orders are there and from units shipped, I can get the number of items in the order. Now I want to predict ...
2
votes
0answers
19 views

LSTM variable period prediction

I'm trying to train a model to predict the final cost of a product being developed over a few months. I have historical data of similar products which will be used for training the model. Some of the ...
1
vote
1answer
55 views

How do I validate this Kalman model for estimation of undocumeted covid cases?

Tensorflow recently made a tutorial titled Estimation of undocumented SARS-CoV2 cases. It replicates 6th March 2020 paper by Li et al titled ...
1
vote
1answer
62 views

Is it possible to forecast the evolution of cars?

Let's say for example that I have a dataset about the cars that a company (e.g. Toyota) produced, over the course of the years 1990 - 2016. Considering that I have already completed the feature ...
1
vote
2answers
201 views

What are suitable datasets for univariate time series forecasting with RNNs, LGBM, TBATS, SARIMA models (topic, frequency, sources)? [closed]

I am currently looking for a suitable dataset (univariate time series) for short-term forecasting using lag features or moving windows of lag features to employ models like LSTM, GRU, SARIMA, LGBM, ...
0
votes
1answer
26 views

Forecasting using Python

I have very less training observations (15). I need to predict 6 months into the future. What forecasting model is best suited for this scenario? Here is how my dataset looks Month | Response ...
2
votes
2answers
61 views

What is the best way to normalize a set of datasets

I have a data set that contains the same Time series "Sensor readings" for different days and I want to make a deep learning model to predict these values. What I did was I splatted the data ...
0
votes
0answers
58 views

Why does the forecasting of this LSTM model look like a steady line?

This is a multivariate multistep problem using LSTM NN model. I am trying to forecast one variable by means of the other variables. However, the forecasting output looks like a horizontal line. Kindly ...
0
votes
0answers
13 views

Using Vector Auto Regression for multiple time series at once

Say I have a dataframe like so: ...
2
votes
1answer
104 views

1st order Taylor Series derivative calculation for autoregressive model

I wrote a blog post where I calculated the Taylor Series of an autoregressive function. It is not strictly the Taylor Series, but some variant (I guess). I'm mostly concerned about whether the ...
1
vote
0answers
33 views

Web scraping using Beatiful Soup

I have this code and I wanna extract holidays, petrol and temperature but I don't know where is the problem. I need your help as soon as possible, please. I want to add this extraction to my dataset ...
0
votes
1answer
36 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
0
votes
1answer
42 views

LSTM's for timeseries with additional regressors

I have a dataset consisting of the weekly sales of 3,000 stores over the past 5 years, and have constructed a LSTM to forecast the next year of sales, given the previous year of sales. At each ...
1
vote
0answers
36 views

Why are Neural Network predictions “correct”, but offset from true value? Not using any past lagged values

I recently asked a similar question, but didn't get a response that really addressed/fixed the issue. Additionally, I've done some more work since then. I'm sorry for the long question below, I just ...
1
vote
2answers
126 views

What are some good methods to forecast future revenue on categorical and value based data?

I have monthly snapshots (3 years) of all the contract data. It includes following information: Contract status [Categorical]: Proposed, tracked, submitted, won, lost, etc Contract stages [...
0
votes
1answer
29 views

Best Approach to Forecasting Numerical Value Based on time series and categorical data?

Consider a dataset of thousands of car repairs that have been performed. In simplest of terms, the columns to consider are the time of year when it was broken (seasonal changes in demand for car ...
-3
votes
1answer
30 views

Accuracy of a forecasting model for prediction of COVID-19 occurence

My goal is to find the best performing forecasting model for the occurrence of COVID-19 in Toronto. I pre-train the network with data on the occurrence of SARS in ten countries and Toronto. Then I ...