Questions tagged [arima]

arima (autoregressive integrated moving average.) It's a model used in data science to measure events that happen over a period of time. The model is used to understand past data or predict future data in time series.

Filter by
Sorted by
Tagged with
1 vote
1 answer
64 views

Why so discrepancy between ARIMA and LSTM in time series forecasting?

I have this time series below, that I divided into train, val and test: Basically, I trained an ARIMA and an LSTM on those data, and results are completely different, in terms of prediction: ARIMA: ...
  • 209
0 votes
0 answers
20 views

Reformulation of ARIMA for an ML context

Is the following approach to reformulate the ARIMA model for $\varepsilon_{t}$ correct? I want to minimize $\sum{\varepsilon^{2}_{t}}$ but I only have a time series $y_t$ and the two are coupled such ...
0 votes
0 answers
30 views

How to make ARIMA model out of sample forecasts with exog Fourier terms using weekly data? (Python)

I'm a bit confused on how to make out-of-sample predictions if I have Fourier terms included in my ARIMA model. I am using Fourier terms to model annual seasonality as per the advice given in ...
  • 1
4 votes
4 answers
814 views

Python (S)ARIMA models completely wrong

I have some time series, like this one: I want to predict future values, so I splitted in train/test (70/30) and I created several ARIMA models, however they are all completely wrong (or maybe I am ...
  • 209
0 votes
0 answers
6 views

Which data to use to plot ACF/PACF while determining the ARIMA parameters?

I am confused about how to plot ACF/PACF. I do STL decomposition to time series data and then take the residuals and plot ACF/PACF to determine the ARIMA parameters. Most examples, however, take the ...
  • 121
2 votes
1 answer
54 views

ARIMA predictions look shifted by one unit of time

I am using statsmodels ARIMA (1,2,1) to predict the monthly demand for a product. The predictions look like they are shifted to the right by one month. I wonder if the statsmodels.ARIMA.Residuals....
  • 121
0 votes
2 answers
32 views

How should a dataset looks like for Time series forecasting

What should a dataset look like for time series forecasting? Can I do time series forecasting with a dataset that contains apartments from ad sites obtained with: web scraping from 2018 to 2021 13 ...
0 votes
1 answer
133 views

Which dataset for multivariate time series forecasting

I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ? Can I use a dataset ...
0 votes
0 answers
18 views

Spike and dip cleaning in Big Query ML ARIMA_PLUS

If there is sales of a product spiking once every year (let us say every year in May). Will the ARIMA_PLUS model of Bigquery clean every year May sales as spike ...
0 votes
0 answers
54 views

Negative forecasts in ARIMA

Can ARIMA (specifically BigQueryML ARIMA_PLUS) give negative forecasts even if the training data has only 0 or positive values?
0 votes
0 answers
75 views

Confidence/prediction interval vs lower and upper bounds in ARIMA_PLUS big query

How is CI intervals in ML.forecast and lower-upper bounds in ML.Detect_Anomalies different for Arima_plus model in BigqueryML ?
0 votes
0 answers
56 views

Anomaly detection and root cause analysis

ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. ...
0 votes
0 answers
12 views

Why ARMA models needs stationarity

I am trying to find why ARMA models needs stationarity to work, I have simulated some nonstationary processes and the estimated parameters (point estimates) seems to be very similar to the actual ones....
  • 3
0 votes
0 answers
9 views

Regression AR(p) models and stationarity

I am starting to learn time series models besides the expoential smoothing ones and I got a few questions that I am struggling with. If I have a stationary time series wich follows an AR(1) process, ...
  • 3
0 votes
1 answer
121 views

Applicability of ARIMA model on non stationary data

I have a time series dataset that does not have the stationary property. The dataset is monotonically increasing or sometimes showing no change over periods of time. Can I apply the ARIMA model to ...
0 votes
1 answer
29 views

How to handle multi time series data for 10K + items

There are 50 shops and each shop have 30000 items. Goal is to forecast the sale of item based on shop. Forecase the item_cnt_day, for this i dont see this as multi ...
  • 123
0 votes
1 answer
342 views

How to revert np.log(data) and data.diff()?

I have used np.log(data) and then applied data.diff() to transform my data in timeseries model. I have the predictions. How do I ...
1 vote
0 answers
24 views

Does this ARIMA model take seasonality into account?

I'm writing a tutorial on traditional time series forecasting models. One key issue with ARIMA models is that they cannot model seasonal data. So, I wanted to get some seasonal data and show that the ...
0 votes
2 answers
52 views

How can we make forecasts from stationary data

I'm confused about the concept of stationarity. Most definitions require the mean and Variance to be constant 'over any interval'. This statement confuses me, if any interval should have the same mean ...
1 vote
0 answers
49 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 ...
2 votes
0 answers
804 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
1 answer
30 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
1 answer
79 views

ML model to forecast time series data

This question has three sub-parts, answering each of which probably doesn't require huge text. I hope that is okay. I'm trying to understand time series prediction using ML. I have the target variable ...
3 votes
1 answer
99 views

How to know if a time series sequence is predictibale or just random (Univariate time series prediction)?

I'm trying to predict a current value of a variable based on the its previous 10 values. I tried multiple time series approaches including ARIMA, LSTM and linear regression... None of them really ...
0 votes
0 answers
24 views

Best approach for univariate time series predictions?

I have a univariate time series. where I'm trying to predict a current value of a variable based on the previous 10 values of the same variable. I tried three approaches: 1- linear regression where I ...
0 votes
1 answer
517 views

First Differencing to remove seasonality and trends

I am trying to remove seasonality and trends from my time series data. I found this post that said to use df_diff = df.diff().diff(12).dropna() (https://www.tobiolabode.com/blog/2020/12/30/how-to-...
1 vote
1 answer
43 views

Predict the first observations of a time series when order of the model is higher

Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set. Suppose you create an AR (...
  • 209
1 vote
0 answers
33 views

How can i find the accurcy for time series? [closed]

I have to work for the first time with time series and I have some question about this interesting field of machine learning. What I have to do: I should make a forecast for quantity for some special ...
  • 47
1 vote
1 answer
1k views

Python: SARIMAX Model Fits too slow

I have a time series data with the date and temperature records of a city. Following are my observations from the time series ...
4 votes
1 answer
1k views

ARIMA training super slow

I am fitting ARIMA model (from statsmodels) on 20 000 elements dataset on a 24 CPU 200+GB RAM cloud server for over 24 hours now. It loads all the CPU's. But It takes so long... Is it how it works or ...
  • 103
0 votes
0 answers
27 views

ARIMA d Parameter and Explicit Differencing

Can I use only d parameter for ARIMA instead of applying differencing to data before training and applying inverse transform to forecasts in order to get them into original scale? Do libraries like ...
  • 422
0 votes
0 answers
143 views

AR coefficients are not stationary

I have a timeseries data and I want to forecast it by applying ARIMA. After reading data, I decomposed it to analyze its components and get an idea whether it is stationary or not. It seems there is ...
  • 422
1 vote
0 answers
214 views

auto_arima results summary (intercept?)

I ran auto_arima on my model from the library pmdarima and I'm trying to interpret the results printed to the console. I see two examples of similar parameters that yield different results: ARIMA(0,1,...
0 votes
0 answers
433 views

Narrow confidence Interval of forecast

I am new to data science so please accept my apology in advance. I am trying to predict the value using ARIMA. I have got weekly value for the current year. Based on the available weekly values, I ...
0 votes
2 answers
2k 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 ...
3 votes
3 answers
3k views

Best common metric for comparing classic time series forecasting methods (ARIMA/Prophet) with ML approach?

I am new to time series forecasting and looking to compare the performance of ARIMA/Prophet with an XGBoost model in predicting future stock market values based on historical stock market data and ...
  • 197
0 votes
1 answer
91 views

Time series forecast for everyday for till a distant future

I have time-series data for every single day from the 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 ...
  • 133
1 vote
0 answers
11 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 ...
  • 111
2 votes
2 answers
150 views

High error Arima model - Python

I have a time series data. It has daily frequency. I want to forecast the data for the next week or month with an ARIMA model. This is a chart of my time series data: First I use the method ...
2 votes
0 answers
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 ...
1 vote
0 answers
90 views

Understanding lag plot ,ACF plot and auto-correlation plots

I have a data-frame of 2809 rows which is an unevenly spaced time-series ...
3 votes
1 answer
75 views

How to use ARIMA to predict time series?

...
2 votes
0 answers
223 views

ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
  • 121
0 votes
1 answer
146 views

Choosing the periodicity in a SARIMA model

Given the order (P,D,Q,s) of a SARIMA model, s is an integer representing the number of periods in a season. Intuitively, I suppose it would be 12 for monthly data ...
1 vote
1 answer
31 views

Why is my prediction using ARIMA better if I'm using less historic data?

I have a data set containing hourly electricity prices for since 1.01.19 until September. Since the process turned out to be (weakly) stationary, I applied an ARIMA model in Python in order to predict ...
4 votes
2 answers
6k views

Forecasting via LSTM or XGBoost... is it really a forecast or

I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts. This tutorial does a nice job explaining step by step of ...
  • 141
2 votes
1 answer
7k views

XGBoost vs ARIMA for Time Series analysis

Doing time series analysis, I have doubts on choosing the right model. I want to predict the next 30 mins window, from the input dataset which contains the no. of error count for that particular 1 min ...
  • 141
1 vote
1 answer
85 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
1 vote
1 answer
2k views

Categorical variable for Arima

Does Arima support the usage of categorical variable? Some ways to get it working can be using one-hot encoding to represent categorical variables, but I am not sure how good it is.
1 vote
1 answer
404 views

Why MA model order is from acf but not pacf

For MA model in Arima, why the order references acf, but not pacf? The emphasize is why not PACF. In https://towardsdatascience.com/significance-of-acf-and-pacf-plots-in-time-series-analysis-...