Questions tagged [forecasting]
The forecasting tag has no usage guidance.
177
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13 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 ...
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2answers
21 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 ...
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8 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 ...
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46 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 ...
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15 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 ...
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11 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
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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
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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
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1answer
61 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 ...
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2answers
41 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, ...
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0answers
14 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 ...
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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 ...
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2answers
47 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 ...
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26 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 ...
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0answers
19 views
Backtesting - Multiple Train-Test Splits
I am looking to use an auto ML platform for retail forecasting. Will use 3.5 years of sales data.
Our business has changed significantly. Higher margins, fewer incentives and competition has ...
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13 views
1
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1answer
97 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 ...
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0answers
17 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 ...
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1answer
30 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
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1answer
21 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 ...
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23 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 ...
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2answers
57 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
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1answer
17 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 ...
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1answer
22 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 ...
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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 ...
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0answers
27 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
...
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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
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1answer
25 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
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1answer
144 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 ...
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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 ...
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0answers
24 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 ...
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2answers
28 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
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1answer
25 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 ...
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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 ...
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0answers
14 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
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1answer
22 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.
...
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2answers
46 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....
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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 ...
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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
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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
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1answer
28 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 ...
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2answers
427 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 ...
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0answers
9 views
Train one model across multiple multivariate time series of diffrent duration, using categorical metadata
I'm trying to create model for prediction multiple correlated time series features.
Issue is that input dataset consists of a number of "projects" with different duration and different categorical ...
1
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1answer
18 views
Is my model to big? I am trying to predict orders for a company, and I don't know if there are typical values for macroparameters
I am building a model to predict orders, from its time series (univariate), for a company. I am working with 30 layers of 400 LSTM neurons each with the activation function hyperbolic tangent of Yann ...
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0answers
21 views
how do I approach forecasting problems using deep neural networks?
I am new to machine learning in general, and I have been requested to predict a price given a date. I have been trying to make a neural network for the task but it does poorly in the testing set, so I ...
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0answers
23 views
Multi-dimensional Time Series Features
I am new to applying ML to time series data but I do have experience doing general supervised learning. I have time series that is multidimensional (so several variables over time) with one output ...
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0answers
10 views
lags number in multivariate time series analysis correlation
I am trying to calculate the correlation coefficient(Pearson's r) of a financial time series $Y(t)$ and other exogenous variables $X_1(t),..., X_n(t)$. I am trying to understand the impact of my ...
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0answers
20 views
Multiple Values for One Day
I have two questions.
1- I have weather data of 10 turbines and I know their collective production(Power).I also know maximum power a turbine can make. How can I forecast collective production if I ...
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1answer
21 views
I am doing a forecasting for corona virus cases in NY, I have two models and not sure which one i should choose
I am using exponential smoothing and using tableau for forecasting. The first model I included trend and removed seasonality and it predicted the number of cases going up but the quality I got ...
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0answers
85 views
LSTM model for multi-step forecasting with multivariate time series
Im am trying to do a multi-step forecasting with multivariate time series, I have 9 variables (Y,X1,..X8) with 2270 samples for each variable, and I am trying to predict the future values of Y (70 ...
