Questions tagged [forecasting]
The forecasting tag has no usage guidance.
183
questions
0
votes
0answers
16 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
1answer
20 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
40 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
10 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
18 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
61 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
34 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
36 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
18 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
21 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
29 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
15 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
53 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
16 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
0answers
12 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
17 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
118 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, ...
1
vote
0answers
20 views
State of the art in intermittent time series forecasting?
I'm doing a school project with a prediction challenge on intermittent time series data (a lot of zeros). I've tried Triple exponential smoothing, Croston, and LSTMs for forecasting but I would like ...
0
votes
1answer
25 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
50 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
36 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
2
votes
1answer
101 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
19 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
32 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
32 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
26 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
79 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
22 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
26 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 ...
1
vote
2answers
36 views
How can I explain this chart showing 5-days moving average?
I have plotted the frequency of items sold through time, trying to determine the trends by moving average.
I considered a 5-days window. I would like to know if this approach makes sense and how I ...
1
vote
0answers
28 views
Time Series Analysis / Modeling with auto_arima
I recently dived into Time Series and was attempting at doing some data analysis and modeling. I'm using the following dataframe
...
0
votes
0answers
6 views
Ljung-box test on weekly percentage of total quarter bookings
I have a data on the weekly percentage of the total quarter bookings. The data looks as follows (note: weekly percentages add up to 100 for each quarter) :
(not real data)
I used the Ljung-box test ...
0
votes
1answer
51 views
Building Timeseries models for stock trading having multiple stocks
I have gone through some of the tutorials on the timeseries and all of them have taken one stock for the timeseries and tried to forecast it. My dataset contains many stocks for the time period(each ...
0
votes
1answer
380 views
Multivariate, Multi-step LSTM time series forecast
I'm trying to predict the Pollution using a Multivariate and Multi-step LSTM code, I've been following this tutorial.
I've been following the code until the end, but couldn't understand where the ...
0
votes
0answers
6 views
monte carlo simulation for impact to revenue by adjusting credit limits
I've been tasked with prioritizing projects for the year. One of them is to estimate the impact to net revenue if we were to adjust the purchase limits for our users. Our platform is transactional in ...
0
votes
0answers
32 views
RNN: Multiple inputs per time step with categorical variables
I am trying to a build RNN model to forecast daily sales for several different cities and different product segments (categorical features and multiple inputs for each day) along with numerical ...
0
votes
2answers
33 views
What are some good loss functions used to minimize extreme errors in regression and time series forecasting?
E.g. In detriment of a smaller mean error, I want to have fewer big mistakes
I'm working on a time series forecasting task and in some specific cases I don't need perfect accuracy, but the network ...
1
vote
1answer
30 views
best NN architecture for point prediction
I'm training to predict a single value y (continuos in [0,1]) based on a number of variables ...
0
votes
0answers
13 views
Trying to determine ARIMA parameters in time series
Hello I have a stationary time series with ACF and PACF plots as follows. How can I determine the p,q,d parameters with these graphs? Are they the lag values where the graph intersects the upper ...
0
votes
0answers
15 views
Preventing Overfitting with CausalImpact
I am looking to perform causal inference on a fairly limited dataset. The data is summarized monthly over the last few years, so I'm only looking at roughly 12-20 data points per time series. However, ...
0
votes
1answer
23 views
Energy price forecasting on timeseries
I try to predict electricity price based on several factors from historical data (consumption, consumption prognosis, wind power, wind power prognosis). All datasets I retrieve from Nordpool webpage.
...
1
vote
2answers
48 views
Time series forecasting: prediction and forecast far from the reality
Apologies for the awkward title, but I hope to be able to regain your confidence.
Let's start with the final output I got, so at least you can understand why I'm not happy/concerned about the outcome....
0
votes
0answers
10 views
How to make a multivariate forecasting if one of features becomes known for the future with some confidence level, e.g. weather forecast data
Let's assume that we make forecasting of another metric partially based on forecasts of the weather forecast, e.g. of temperature, pressure, then we can potentially obtain those forecasts from one of ...
0
votes
0answers
7 views
How could i change the frequence of Date time from None to a specific frequency
I have mixed data that contains a different date-time value, not daily or weekly and I don't know how to change the frequency from None so I can use the algorithms like Arima to it
2
votes
0answers
18 views
How to automate Seasonal Arima?
I am building Seasonal Arima for more than 10k products. In all the tutorials and blogs mentioned, I need to do the exploration to find the p,d,q values along with seasonality value using the ...
2
votes
1answer
34 views
Formulate multivariate multistep time series forcasting using traditional machine learning, NOT deep learning
How do you represent multivariate multistep data using traditional machine learning? I know this seems like a tailored problem for RNN/LSTM, but I am wondering what the alternative machine learning ...
0
votes
2answers
738 views
LSTM Multivariate time series forecasting with multiple inputs for each time step
I want to predict an output variable for the next day, for each of the users in my dataset. I was thinking of using LSTMs for achieving this.
The dataset
The dataset I am using has multiple inputs ...
